ODSC WEST 2023 | IN-PERSON & VIRTUAL CONFERENCE
AI EXPO & DEMO HALL
AI Solution Showcase | Networking
November, 2023 | Hyatt Regency San Francisco Airport
PARTNERS
TALKS AND PRODUCT DEMOS
NETWORKING EVENTS
ATTENDEES
Learn How to Build AI Better
Want to keep up with the latest AI developments, trends, and insights? Dealing with the build or buy dilemma to grow your business? Seeking to interact with data-obsessed peers and build your network?
Look no further: ODSC AI Expo & Demo Hall is the right destination for You
Expo Hall Topics
Partner sessions offer compelling insights on how to make data science and AI work for your industry. Here are some of the topics you can expect at AI Expo & Demo Hall. Full agenda is coming soon.
Trustworthy Decision Management
What to Consider with Model Ops When Moving to the Cloud?
Getting started with Dask using Saturn Cloud
Privacy-Preserving Machine Learning: Split Learning and Privacy Attacks
An Overview of Arize AI’s ML Observability Platform
Z by HP’s Data Science Solutions
Past Visionaries and Thought Leaders
Bob Foreman
Bob has worked with the HPCC Systems technology platform and the ECL programming language for over a decade and has been a technical trainer for over 30 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote based training.
Relational Dataset Analytics for Clear Customer Insights(Workshop)
Ben Sherman
Ben is a machine learning solutions consultant with W&B. He trains our customers to use W&B and works with them to improve their machine learning workflow. Prior to joining W&B he was training models and developing ml infrastructure for Samsung Research.
ML Tools for Humans(Demo Talk)
John Wasserman
John is a Data Architect at Airbyte where he enjoys helping companies move data from where it’s created to where they want it to live. Before AIrbyte he worked as a Global Solutions Architect at LiveRamp where he helped companies activate data to transform customer experiences. Besides being in the weeds about data, John is an avid bike rider and golfer.
Open Source Powers the Modern Data Stack (Demo Talk)
Aaron Zukoff
Aaron is our Director of Solutions Engineering at Appen. He works closely with the Sales and Solutions teams to manage Fortune 500 deals through the pipeline. Aaron has lived in 7 cities around the world and is a geek at heart. He loves solving problems, breaking new technologies and identifying opportunities where technology can have a real impact on how we get things done.
Peter McGuinness
Peter is VP of Engineering at Mindtech. Peter has many years of experience in semiconductors, with expertise in AI, GPU and VR/AR. Working at companies including Highwai, Imagination Technologies and ST. Peter has also been highly active in Khronos, including chairing the NNEF working group.
Shayan Mohanty
Shayan Mohanty is the CEO and Co-Founder of Watchful, a company that largely automates the process of creating labeled training data. He’s spent over a decade of leading data engineering teams at various companies including Facebook, where he served as lead for the stream processing team responsible for processing 100% of the ads metrics data for all FB products. He is also a Guest Scientist at Los Alamos National Laboratory and has given talks on topics ranging from Automata Theory to Machine Teaching.
Bias is Good: Arguments for Programmatic Labeling(Demo Talk)
Nick Schenone
Nick is a passionate machine learning, data science, and MLOps enthusiast with experience across multiple domains including fraud detection, natural language processing, computer vision, and data mining. Nick holds a BSc. in Cognitive Science with a specialization in ML and Neural Computation from University of California, San Diego. He is an AWS Certified Solutions Architect, and has earned certifications in Python, Pytorch, Apache Airflow, PySpark and other frameworks. Currently, Nick acts as pre-sales MLOps Engineer at Iguazio, where he specializes in helping enterprises create real-world impact with their data science initiatives, with expertise in deployments on AWS, GCP, and Azure as well as on-premise Kubernetes architecture. Nick speaks at global industry events and blogs about MLOps, data science and ML Engineering.
Building an AI App in Under 20 Minutes Using OS MLOps tool MLRun(Demo Talk)
Pete Goddard
Pete spent more than two decades on Wall Street, growing, and running automated trading groups. In 2005, he was the founding CEO of Walleye Capital, a multi-billion-dollar quant fund that derives value at the intersection of real-time data and automated applications. In 2017, Pete and some engineers spun a proprietary data engine out of Walleye, forming an independent company called Deephaven Data Labs. Deephaven is an open-first software shop, delivering a real-time query engine, APIs, UIs, and integrations to the community via open projects designed for diverse teams. Deephaven complements streaming technologies and makes dynamic data easy and accessible.
Real-time Analytics, AI&Apps with Deephaven Data Labs(Demo Talk)
Martin Shell
Martin has over 30 years of experience in Data Science, AI, Decision Optimization. He worked as Consulting Project Manager, Technical Sales, Data Scientist with organizations including ILOG, IBM, Manhattan Associates, Emptoris. He has strong modeling skills in constraint programming, mathematical programming, machine learning. He is skilled in C++, Java, Python. Martin’s main objective is to help organizations identify and deploy analytics that maximize ROI. He was selected as INFORMS Franz Edelman Award finalist. He has studied M.S. in Operations Research from Massachusetts Institute of Technology.
Turning your Data/AI algorithms into full web apps in no time with Taipy (Demo Talk)
How to Build Stunning Data Science Web applications in Python – Taipy Tutorial(Workshop)
Sandeep Agrawal, PhD
Sandeep Agrawal leads the HeatWave Machine Learning (HeatWave ML) project within MySQL HeatWave. HeatWave ML is the product of years of research and advanced development, and aims to help both data scientists and non-data scientists quickly apply ML to a given problem. Prior to HeatWave, Sandeep led the Oracle AutoML project within Oracle labs, creating a state-of-the-art distributed AutoML engine. He is passionate about Machine Learning and Systems Architecture, and a project like HeatWave ML that combines the two is heaven for him. Prior to Oracle, he completed his PhD in Computer Science from Duke University in 2015.
Hugo Shi
Hugo Shi a data science leader with 15 years of experience with data science and software projects at companies ranging from JP Morgan to the Chicago Trading Company. He is the CTO and co-founder of Saturn Cloud where he helps to make sure that Saturn Cloud is secure, scalable and easy to use for all data science teams. Hugo has a PhD in Signal Processing and his academic research focused on iterative reconstruction algorithms in medical imaging.
Data Science Platforms are Bad(Demo Talk)
Lucas Chatham
Lucas is the Product Manager for Ground Control, iMerit’s single source of truth platform for managing data annotation workflows through reporting, analytics, and insights. Prior to iMerit, he designed and launched mapping technology for self-driving cars and developed electronics systems for high-performance vehicles. When not working in the trenches of machine learning, either as an engineer or Product Manager, you can find Lucas experimenting with ML in a variety of side projects, like using computer vision to optimize human biomechanics.
Stuart Laurie
As a member of the Neo4j Field Engineering team, Stuart brings 15 years of experience helping many Global 2000 organizations solve their business challenges leveraging semantic technologies, natural language processing, search and graphs. In addition, he has experience across a wide range of industries, including healthcare, finance, manufacturing and retail. Based in the Bay Area, Stuart works with large enterprise companies including Wells Fargo, eBay, Visa, Adobe, Genentech, Kaiser and Cisco.
Neo4j Demo: A Graph Data Science Framework for the Enterprise(Demo Talk)
Justin Emerson
Justin Emerson is a Principal Technology Evangelist at Pure Storage focused on the FlashBlade product portfolio. He joined Pure in 2020 as a FlashBlade Data Architect for the San Francisco Bay Area. Prior to that, he worked at storage-focused reseller partners for more than a decade.
Turbo Boost Workflows for AI, ML, DevOps and EDA with Modern File Utilities(Demo Talk)
AI TCO (Total Cost of Ownership) Considerations from Pilot to Production Scale(Talk)
Jake Bengtson
Jake is currently working as a Senior Product Marketing Manager over ML Lifecycle products at Cloudera. Before joining Cloudera, Jake worked as a Data Scientist and Solution Architect at ExxonMobil. Additionally, he worked as a Senior Data Scientist at FarmersEdge. Before starting his professional career, Jake obtained his bachelor’s and master’s degree from Brigham Young University. When he isn’t working, Jake enjoys skiing, golfing, and spending time with his family in the mountains.
Forecasting Crypto Currency Prices with Cloudera Applied Machine Learning Prototypes(Demo Talk)
Brandon Chen
Bio Coming Soon!
Oryan Omer
Oryan is a ֿLead Software Engineer with a passion for Machine Learning and DevOps, with 7 years of experience developing services for production and development environments and leading teams.
Data-driven ML Retraining with Production Insights(Demo Talk)
Jerry Yurchisin
Mr. Yurchisin has over ten years’ experience applying operations research, machine learning, statistics, and data visualization to improve decision making. Before joining Gurobi, Jerry (who also goes by Jerome) was a Senior Consultant at OnLocation, Inc. where he customized several linear programming models within the National Energy Modeling System (NEMS) to analyze implementing specific energy policies and utilizing new technologies.
Prior to OnLocation, Jerry was an Operations Research Analyst & Data Scientist at Booz Allen Hamilton for over seven years. There he formulated scheduling and staffing integer programming models for the US Coast Guard, as well as led a project to quantify the maritime risks of offshore energy installations with the Research & Development Center. Further, Jerry was the technical lead on several Coast Guard studies including Living Marine Resources and Maritime Domain Awareness, providing statistical analysis and building supervised and unsupervised machine learning models. He also performed statistical analyses, machine learning modeling, and data visualization for cyberspace directorates at DoD and DHS.
Jerry has several years of experience teaching a wide variety of college-level mathematics and statistics courses and has a passion for education. He also enjoys golfing, biking, and writing about sports from an analytics point of view. He lives in Alexandria, Virginia with his wife, son, and two dogs.
Jerry holds B.S., Ed. and M.S., Mathematics degrees from Ohio University and an M.S. in Operations Research and Statistics from The University of North Carolina at Chapel Hill.
From Data to Decisions: Make your Machine Learning Models mean more with Mathematical Optimization (Demo Talk)
Alex Duncan
Alex first joined HP as a program manager on the Advanced Compute Solutions OEM team, providing extended life hardware solutions to critical partners across industry. Now, Alex is the product manager for Data Science Workstations. Alex manages Data Science Hardware, operating systems, and the Z by HP Data Science Stack Manager. Prior to HP, Alex received his MBA from the University of Texas-Austin and served in the United States Army.
Data Science at 200mph, How HP Data Science Powers Winning Racing, Presented by HP Inc(Demo Talk)
Steve Sutton
Steve is the Software Engineering Lead for HP’s Data Science Solutions Team. For two years he’s been curating HP’s Data Science Stack and building processes to ensure compatibility across HP workstations. He studied Computer Science at Colorado State University, and no matter the season – he tries his hardest to get lost in the Rocky Mountains.
Data Science at 200mph, How HP Data Science Powers Winning Racing, Presented by HP Inc(Demo Talk)
Savita Mittal
Savita is Data & AI evangelist based out of San Francisco Bay Area, USA. Savita brings 15 years of experience as a Technology professional during which she worked on Microsoft platform architecting and developing applications, automating solutions and integrations across Azure, M365, Power Platform and Teams.
She believes AI is a game changing development in human history which can solve some of the most daunting challenges humanity faces today. This idea drives her to relentlessly engage with customers, educate them on the potential of AI, showcase practical use cases and ultimately get them excited and thinking about how they can use AI to solve their organization’s pressing challenges.
Azure AI Powered Global Translator(Demo Talk)
Chase Christensen
Chase is a solutions architect at Arrikto with a passion for connecting people to technical solutions that can prevent them from wasting precious time and mental energy- solving the same problems over and over. Chase is a certified Kubernetes Administrator, Developer, and Security Specialist who works to help clients reduce MLOps friction and toil while ensuring the “non-negotiables” are enforced to provide the best return on their production models.
How Far Left Can You Shift? The Tension Between Data Science and ML Engineering(Talk)
Personal to Product to Platform: Reporting Your Results with Kubeflow(Demo Talk)
Souheil Inati, PhD
Souheil is the Head of Field Data Science at Arrikto where he helps build machine learning solutions for clients. Previously, Souheil worked at Freddie Mac and Capital One where he built models and machine learning platforms. Prior to becoming a data scientist, he spent 15 years in academia working on MRI and Brain Imaging. Souheil holds a BS and PhD in Physics from Yale and MIT respectively.
How Far Left Can You Shift? The Tension Between Data Science and ML Engineering(Talk)
Personal to Product to Platform: Reporting Your Results with Kubeflow(Demo Talk)
Kyle Kirwan
Kyle Kirwan is the co-founder and CEO of Bigeye, the data observability company. Before starting Bigeye, Kyle led the development of Uber’s internal data operations tools: a data catalog, data lineage collector, data pipeline testing, and incident management tools. He enjoys hiking and tiki bars.
Session Title: Data Observability for Data Science Teams
Abstract: When putting models into production it’s critical to know how they’re performing over time. As the last mile of the data pipeline, models can be impacted by a variety of issues, often outside the control of the data science team. “Observability” promises to help teams detect and prevent issues that could impact their models—but what is observability vs. data observability vs. ML observability? Get practical answers and recommendations from Kyle Kirwan, former product leader for Uber’s metadata tools, and founder of data observability company, Bigeye.
Mike Wong
Mike Wong is a Solutions Engineer at Unravel Data helping customers navigate the challenges of the modern data economy and optimize complex data stack. Previously, he spent nearly 20 years as a solution architect in a range of technology roles from PLM to Hadoop. His robust experience in the DataOps domain allows Mike to help customers achieve their vision with data applications and infrastructure.
Empowering DevOps for Data Teams(Demo Talk)
Florian Jacta
Florian Jacta is a specialist of Taipy, a low-code open-source Python package enabling any Python developers to easily develop a production-ready AI application. Package pre-sales and after-sales functions. He is data Scientist for Groupe Les Mousquetaires (Intermarche) and ATOS. He developed several Predictive Models as part of strategic AI projects. Also, Florian got his master’s degree in Applied Mathematics from INSA, Major in Data Science and Mathematical Optimization.
How to build stunning Data Science Web applications in Python – Taipy Tutorial(Workshop)
Demo Talk Session Title: Turning your Data/AI Algorithms into full web apps in no time with Taipy
Abstract:
In the Python open-source ecosystem, many packages are available that cater to:
– the building of great algorithms
– the visualization of data
Despite this, over 85% of Data Science Pilots remain pilots and do not make it to the production stage.
With Taipy, a new open-source Python framework, Data Scientists/Python Developers are able to build great pilots as well as stunning production-ready applications for end-users.
Taipy provides two independent modules: Taipy GUI and Taipy Core.
In this talk, we will demonstrate how:
Taipy-GUI goes way beyond the capabilities of the standard graphical stack: Gradio, Streamlit, Dash, etc.
Taipy Core fills a void in the standard Python back-end stack.
Vincent Gosselin
Vincent has 30+ years as AI specialist with ILOG and IBM. He has mentored several Data Science teams. Vincent has designed/modeled several major AI projects for customers such as Samsung. Electronics, McDonald’s, Dassault Aviation, Carhartt, Toyota, TSMC, Disney, etc. He is skilled in Mathematical Modeling, Machine Learning, Time Series prediction. He has strong experience in Manufacturing, Retail & Logistics industries. His main objective is to “Help companies go beyond AI pilots and be successful in bringing AI to their end-users”. He received his Msc in Comp. Science & AI from Paris-Saclay University.
Turning your Data/AI Algorithms into Full Web Apps in no Time with Taipy(Demo Talk)
Salil Pradhan
Salil Pradhan is a Product Manager in MySQL HeatWave team. His interests include distributed data processing, machine learning, cloud computing, middleware technologies as well as application areas such as Marketing Automation and Supply Chain Management.
Audrey Reznik
Audrey Reznik has been in the IT industry (private and public sectors) for 27 years in multiple verticals. In the last 4 years, she worked as a Data Scientist at ExxonMobil where she created a Data Science Enablement team to help data scientists easily deploy ML models in a Hybrid Cloud environment. Audrey was instrumental in educating scientists about what the OpenShift platform was and how to use OpenShift containers (images) to organize, run, and visualize data analysis results. Audrey now works as a Data Scientist with the Red Hat OpenShift Data Science Team where she is focused on next-generation applications. She is passionate about Data Science and in particular the current opportunities with ML and Federated Data.
MLOPs GItOps/Pipelines(Demo Talk)
Dancing with Data Science and Security on the Edge(Workshop)
Yotam Azriel
Yotam is a machine learning and deep learning expert with extensive hands-on experience in neural network development. Prior to co-founding Tensorleap, Yotam developed and led AI and Big Data projects from research to production for companies in the automotive and other sectors, as well as developing machine learning algorithms for large government projects, including the Soreq Nuclear Research Center (Israel).
Unleash your Neural Networks with Applied Explainability(Demo Talk)
Virtual | Keynote | NLP | All Levels
As natural language processing now permeates many different applications, its practical use is unquestionable. However, at the same time NLP is still imperfect, and errors cause everything from minor inconveniences to major PR disasters. Better understanding when our NLP models work and when they fail is critical to the efficient and reliable use of NLP in real-world scenarios. So how can we do so? In this talk I will discuss two issues: automatic evaluation of generated text, and automatic fine-grained analysis of NLP system results, which are some first steps towards a science of NLP model evaluation…more details
Graham Neubig is an associate professor at the Language Technologies Institute of Carnegie Mellon University. His research focuses on multilingual natural language processing, natural language interfaces to computers, and machine learning methods for NLP, with the final goal of every person in the world being able to communicate with each-other, and with computers in their own language. He also contributes to making NLP research more accessible through open publishing of research papers, advanced NLP course materials and video lectures, and open-source software, all of which are available on his web site.
Virtual | Keynote
Session Abstract Coming Soon!
Jun Zeng is HP’s Distinguished Technologist and founding manager of the 3D Digital Twin group. Jun has 20 years of industrial experiences in creating and commercializing software for improving cyber-physical systems. His publications include a co-edited book on computer-aided Design and a co-authored book on digital factory, and 50+ peer-reviewed papers. He has 58 U.S. patents granted and more pending. His academic training includes Ph.D. in mechanical engineering and M.S. in computer science, both from Johns Hopkins University. He is ACM member, and IEEE senior member.
In-person | Keynote | Machine Learning | Deep Learning | All Levels
Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Managing such sharing is one of the classical goals of microeconomics, and it is given new relevance in the modern setting of large, human-focused datasets, and in data-analytic contexts such as classifiers and recommendation systems…more details
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, and biological sciences; in recent years, he has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines, and applications to problems in distributed computing systems, natural language processing, signal processing, and statistical genetics. Previously, he was a professor at MIT. Michael is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science, the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA, and SIAM. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. Michael holds a master’s degree in mathematics from Arizona State University and a PhD in cognitive science from the University of California, San Diego.
In-person | Keynote | Machine Learning | Deep Learning | All Levels
Over the past decade the computation demands of machine learning (ML) workloads have grown much faster than the capabilities of a single processor, including hardware accelerators such as GPUs and TPUs. As a result researchers and practitioners have been left with no choice but to distribute these workloads. Unfortunately, developing distributed applications is very challenging. In this talk I will present two projects we developed at UC Berkeley, Ray (https://github.com/ray-project/ray) and Alpa (https://github.com/alpa-projects/alpa), that dramatically simplify scaling ML workloads..more details
Ion Stoica is a Professor in the EECS Department at University of California at Berkeley. He does research on cloud computing and networked computer systems. Past work includes Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an ACM Fellow and has received numerous awards, including the SIGOPS Hall of Fame Award (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001). In 2013, he co-founded Databricks a startup to commercialize technologies for Big Data processing, and in 2006 he co-founded and Conviva, a startup to commercialize technologies for large scale video distribution.
Virtual | Keynote | Responsible AI and Social Good
AI is ever more ubiquitous in our lives but all countries are not created equal in their access to or use of AI. Likewise all countries and businesses do not adhere to the same regulatory frameworks or opinions on governance. Yet all companies would benefit from knowing where they stand so that investment in technology is not ultimately wasted. Likewise, access to AI is being used as a geopolitical tool. What lessons are we able to draw and adopt now and how might this thinking mature into the future…more details
Kay Firth-Butterfield is Head of Artificial Intelligence and a member of the Executive Committee at the World Economic Forum and is one of the foremost experts in the world on the governance of AI. She is a Barrister, former Judge and Professor, technologist and entrepreneur who has an abiding interest in how humanity can equitably benefit from new technologies, especially AI. Kay is an Associate Barrister (Doughty Street Chambers), Master of the Inner Temple, London and serves on the Lord Chief Justice’s Advisory Panel on AI and Law. She co-founded AI Global and was the world’s first Chief AI Ethics officer in 2014 and created the AIEthics twitter hashtag. Kay is Vice-Chair of The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems and was part of the group which met at Asilomar to create the Asilomar AI Ethical Principles. She is on the Polaris Council for the Government Accountability Office (USA), the Advisory Board for UNESCO International Research Centre on AI and AI4All. Kay has advanced degrees in Law and International Relations and regularly speaks to international audiences addressing many aspects of the beneficial and challenging technical, economic and social changes arising from the use of AI. She has been consistently recognized as a leading woman in AI since 2018 and was featured in the New York Times as one of 10 Women Changing the Landscape of Leadership.
In-person | Keynote | Machine Learning | Deep Learning | All Levels
In this session, we will dive deep into Feathr, taking you on a journey into this scalable open-source feature store which has now joined the Linux Foundation AI and Data ecosystem. Feathr has been battle-tested in LinkedIn powering high scale ML applications, supporting 100s of training and inferencing pipelines. This enables feature sharing among teams, leading to significant business metrics gain…more details
Dr. Inchiosa’s passion for AI drives his work as Principal Data Scientist Manager in Azure Data’s Advanced Workload Engineering team, where he leads a team of data scientists focused on AI-led co-innovation engagements with strategic customers and partners. Previously, Mario served as Revolution Analytics’ Chief Scientist and as Analytics Architect in IBM’s Big Data organization, where he worked on advanced analytics in Hadoop, Teradata, and R. Prior to that, Mario was US Chief Scientist in Netezza Labs, bringing advanced analytics and R integration to Netezza’s SQL-based data warehouse appliances. He also served as US Chief Science Officer at NuTech Solutions, a computer science consultancy specializing in simulation, optimization, and data mining, and Senior Scientist at BiosGroup, a complexity science spin-off of the Santa Fe Institute. Mario holds Bachelor’s, Master’s, and PhD degrees in Physics from Harvard University. He has been awarded four patents and has published over 30 research papers, earning Publication of the Year and Open Literature Publication Excellence awards.
Virtual | Keynote | Machine Learning | Deep Learning | All Levels
In this session, we will dive deep into Feathr, taking you on a journey into this scalable open-source feature store which has now joined the Linux Foundation AI and Data ecosystem. Feathr has been battle-tested in LinkedIn powering high scale ML applications, supporting 100s of training and inferencing pipelines. This enables feature sharing among teams, leading to significant business metrics gain…more details
Dr. Inchiosa’s passion for AI drives his work as Principal Data Scientist Manager in Azure Data’s Advanced Workload Engineering team, where he leads a team of data scientists focused on AI-led co-innovation engagements with strategic customers and partners. Previously, Mario served as Revolution Analytics’ Chief Scientist and as Analytics Architect in IBM’s Big Data organization, where he worked on advanced analytics in Hadoop, Teradata, and R. Prior to that, Mario was US Chief Scientist in Netezza Labs, bringing advanced analytics and R integration to Netezza’s SQL-based data warehouse appliances. He also served as US Chief Science Officer at NuTech Solutions, a computer science consultancy specializing in simulation, optimization, and data mining, and Senior Scientist at BiosGroup, a complexity science spin-off of the Santa Fe Institute. Mario holds Bachelor’s, Master’s, and PhD degrees in Physics from Harvard University. He has been awarded four patents and has published over 30 research papers, earning Publication of the Year and Open Literature Publication Excellence awards.
In-person | Keynote | NLP | Machine Learning | Deep Learning | All Levels
What if computers can truly converse with us in our native tongue? Computers will transform into effective, personalized assistants for everybody. Commercial chatbots today are notoriously brittle as they are hardcoded to handle a few possible choices of user inputs. Recently introduced large language neural models, such as GPT-3, are remarkably fluent, but they are prone to hallucinations, often producing incorrect statements. This talk describes how we can tame these neural models into robust, trustworthy, and cost-effective conversational agents across all industries and languages…more details
Monica Lam is a Professor in the Computer Science Department at Stanford University since 1988. She is the faculty director of the Open Virtual Assistant Lab (OVAL). She received a B.Sc. from University of British Columbia in 1980 and a Ph.D. in Computer Science from Carnegie Mellon University in 1987. Monica is a Member of the National Academy of Engineering and an ACM Fellow. She is a co-author of the popular text Compilers, Principles, Techniques, and Tools (2nd Edition), also known as the Dragon book. Professor Lam’s current research is on conversational virtual assistants with an emphasis on privacy protection. Her research uses deep learning to map task-oriented natural language dialogues into formal semantics, represented by a new executable programming language called ThingTalk. Her Almond virtual assistant, trained on open knowledge graphs and IoT API standards, can be easily customized to perform new tasks. She is leading an Open Virtual Assistant Initiative to create the largest, open, crowdsourced language semantics model to promote open access in all languages. Her decentralized Almond virtual assistant that supports fine-grain sharing with privacy has received Popular Science’s Best of What’s New Award in Security in 2019.
Prof. Lam is also an expert in compilers for high-performance machines. Her pioneering work of affine partitioning provides a unifying theory to the field of loop transformations for parallelism and locality. Her software pipelining algorithm is used in commercial systems for instruction level parallelism. Her research team created the first, widely adopted research compiler, SUIF. Her contributions in computer architecture include the CMU Warp Systolic Array and the Stanford DASH Distributed Memory Multiprocessor. She was on the founding team of Tensilica, now a part of Cadence.
She received an NSF Young Investigator award in 1992, the ACM Most Influential Programming Language Design and Implementation Paper Award in 2001, an ACM SIGSOFT Distinguished Paper Award in 2002, the ACM Programming Language Design and Implementation Best Paper Award in 2004, the ACM SIGARCH/SIGPLAN/SIGOPS ASPLOS Influential Paper Awards in two consecutive years, 2021 and 2022. She was the author of two of the papers in “20 Years of PLDI–a Selection (1979-1999)”, and one paper in the “25 Years of the International Symposia on Computer Architecture”. She received the University of British Columbia Computer Science 50th Anniversary Research Award in 2018.
In-person | Demo Talk | All Levels
In this talk, we’ll explore the ways a time series platform supports data scientists. We’ll learn how you could use Telegraf open source collection agent to perform forecasting at the edge. We’ll explore how you can use Flux query language to prepare and clean your data as well as some preliminary data analysis. Next, we’ll learn about integrations with Jupyter and Zeppelin notebooks. Finally, we’ll cover some statistical properties of time series and some general recommendations for forecasting and anomaly detection algorithm selections…more details
Zoe Steinkamp is a developer Advocate for influxData. She was a front end software engineer for over 6 years before she moved into a developer advocate role. She has been with InfluxDB for over 3 years and she looks forward to sharing her knowledge of the platform and databases. She enjoys learning about awesome new technologies and doing at home tech projects to help make her life as well as other people’s lives easier. Her passions besides new technology include traveling and gardening.
In-person | Demo Talk | All Levels
When every millisecond and decision matters in racing, having the right data when you need it is critical. Z by HP Data Science workstations, mobile workstations, displays, hardware and software solutions help Hendrick Motorsports analyze, model, and simulate data with greater speed and accuracy. From high-end workstations to stack management to remote solutions, HP Data Science compute solutions help Hendrick Motorsports find their edge, on and off the track…more details
Alex first joined HP as a program manager on the Advanced Compute Solutions OEM team, providing extended life hardware solutions to critical partners across industry. Now, Alex is the product manager for Data Science Workstations. Alex manages Data Science Hardware, operating systems, and the Z by HP Data Science Stack Manager. Prior to HP, Alex received his MBA from the University of Texas-Austin and served in the United States Army.
Steve is the Software Engineering Lead for HP’s Data Science Solutions Team. For two years he’s been curating HP’s Data Science Stack and building processes to ensure compatibility across HP workstations. He studied Computer Science at Colorado State University, and no matter the season – he tries his hardest to get lost in the Rocky Mountains.
In-person | Keynote
Session Abstract Coming Soon!
Jun Zeng is HP’s Distinguished Technologist and founding manager of the 3D Digital Twin group. Jun has 20 years of industrial experiences in creating and commercializing software for improving cyber-physical systems. His publications include a co-edited book on computer-aided Design and a co-authored book on digital factory, and 50+ peer-reviewed papers. He has 58 U.S. patents granted and more pending. His academic training includes Ph.D. in mechanical engineering and M.S. in computer science, both from Johns Hopkins University. He is ACM member, and IEEE senior member.
In-Person | Demo Talk | All Levels
MLRun is an open-source MLOps orchestration framework. It exists to accelerate the integration of AI/ML applications into existing business workflows. MLRun introduces Data Scientists to a simple Python SDK that transforms their code into a production-quality application. It does so by abstracting the many layers involved in the MLOps pipeline. Developers can build, test, and tune their work anywhere and leverage MLRun to integrate with other components of their business workflow…more details
Nick is a passionate machine learning, data science, and MLOps enthusiast with experience across multiple domains including fraud detection, natural language processing, computer vision, and data mining. Nick holds a BSc. in Cognitive Science with a specialization in ML and Neural Computation from University of California, San Diego. He is an AWS Certified Solutions Architect, and has earned certifications in Python, Pytorch, Apache Airflow, PySpark and other frameworks. Currently, Nick acts as pre-sales MLOps Engineer at Iguazio, where he specializes in helping enterprises create real-world impact with their data science initiatives, with expertise in deployments on AWS, GCP, and Azure as well as on-premise Kubernetes architecture. Nick speaks at global industry events and blogs about MLOps, data science and ML Engineering.
Virtual | Demo Talk | All Levels
Azure AI Powered Global Translator App allows for easy document translation via both language and audio preferences through various Microsoft platforms. This app is for everyone e.g., healthcare, government, defense & intelligence, education, public safety & justice, and utilities, etc., who have a need to engage with citizens more directly and deeply through cloud-based audiovisual translation services and also looking for easier ways to access the digital content…more details
Savita is Data & AI evangelist based out of San Francisco Bay Area, USA. Savita brings 15 years of experience as a Technology professional during which she worked on Microsoft platform architecting and developing applications, automating solutions and integrations across Azure, M365, Power Platform and Teams.
She believes AI is a game changing development in human history which can solve some of the most daunting challenges humanity faces today. This idea drives her to relentlessly engage with customers, educate them on the potential of AI, showcase practical use cases and ultimately get them excited and thinking about how they can use AI to solve their organization’s pressing challenges.
Virtual | Demo Talk | All Levels
Oracle MySQL HeatWave is a fully managed database service, powered by the integrated HeatWave in-memory query accelerator. It’s the only cloud database service that combines transactions, analytics, and machine learning services into one MySQL Database, delivering real-time, secure analytics without the complexity, latency, and cost of ETL duplication. HeatWave Machine Learning (HeatWave ML) fully automates the process to train a model, generate inferences and invoke explanations, all without extracting data or model out of the database. The user can use familiar SQL interfaces to invoke all the machine learning capabilities. HeatWave ML leverages Oracle AutoML which automates model generation by replacing complex and time-consuming tasks such as data preprocessing, algorithm selection, feature selection and hyperparameter optimization that a data scientist is otherwise expected to perform. With MySQL HeatWave ML, developers and data analysts can build, train, and explain machine learning models in a fully automated way—25X faster than Amazon Redshift ML at 1% of the cost…more details
Sandeep Agrawal leads the HeatWave Machine Learning (HeatWave ML) project within MySQL HeatWave. HeatWave ML is the product of years of research and advanced development, and aims to help both data scientists and non-data scientists quickly apply ML to a given problem. Prior to HeatWave, Sandeep led the Oracle AutoML project within Oracle labs, creating a state-of-the-art distributed AutoML engine. He is passionate about Machine Learning and Systems Architecture, and a project like HeatWave ML that combines the two is heaven for him. Prior to Oracle, he completed his PhD in Computer Science from Duke University in 2015.
Salil Pradhan is a Product Manager in MySQL HeatWave team. His interests include distributed data processing, machine learning, cloud computing, middleware technologies as well as application areas such as Marketing Automation and Supply Chain Management.
In-Person | Demo Talk | All Levels
Oracle MySQL HeatWave is a fully managed database service, powered by the integrated HeatWave in-memory query accelerator. It’s the only cloud database service that combines transactions, analytics, and machine learning services into one MySQL Database, delivering real-time, secure analytics without the complexity, latency, and cost of ETL duplication. HeatWave Machine Learning (HeatWave ML) fully automates the process to train a model, generate inferences and invoke explanations, all without extracting data or model out of the database. The user can use familiar SQL interfaces to invoke all the machine learning capabilities. HeatWave ML leverages Oracle AutoML which automates model generation by replacing complex and time-consuming tasks such as data preprocessing, algorithm selection, feature selection and hyperparameter optimization that a data scientist is otherwise expected to perform. With MySQL HeatWave ML, developers and data analysts can build, train, and explain machine learning models in a fully automated way—25X faster than Amazon Redshift ML at 1% of the cost…more details
Sandeep Agrawal leads the HeatWave Machine Learning (HeatWave ML) project within MySQL HeatWave. HeatWave ML is the product of years of research and advanced development, and aims to help both data scientists and non-data scientists quickly apply ML to a given problem. Prior to HeatWave, Sandeep led the Oracle AutoML project within Oracle labs, creating a state-of-the-art distributed AutoML engine. He is passionate about Machine Learning and Systems Architecture, and a project like HeatWave ML that combines the two is heaven for him. Prior to Oracle, he completed his PhD in Computer Science from Duke University in 2015.
Salil Pradhan is a Product Manager in MySQL HeatWave team. His interests include distributed data processing, machine learning, cloud computing, middleware technologies as well as application areas such as Marketing Automation and Supply Chain Management.
Virtual | Demo Talk | All Levels
When every millisecond and decision matters in racing, having the right data when you need it is critical. Z by HP Data Science workstations, mobile workstations, displays, hardware and software solutions help Hendrick Motorsports analyze, model, and simulate data with greater speed and accuracy. From high-end workstations to stack management to remote solutions, HP Data Science compute solutions help Hendrick Motorsports find their edge, on and off the track…more details
Alex first joined HP as a program manager on the Advanced Compute Solutions OEM team, providing extended life hardware solutions to critical partners across industry. Now, Alex is the product manager for Data Science Workstations. Alex manages Data Science Hardware, operating systems, and the Z by HP Data Science Stack Manager. Prior to HP, Alex received his MBA from the University of Texas-Austin and served in the United States Army.
Steve is the Software Engineering Lead for HP’s Data Science Solutions Team. For two years he’s been curating HP’s Data Science Stack and building processes to ensure compatibility across HP workstations. He studied Computer Science at Colorado State University, and no matter the season – he tries his hardest to get lost in the Rocky Mountains.
Virtual | Demo Talk | All Levels
In this session, you will learn about innovative applied explainability techniques that will allow you to overcome familiar challenges in neural network development, such as creating balanced datasets, testing, edge case detection, troubleshooting, and auditing your models. We will deep dive into specific use cases so you can learn how to apply these techniques to your own models to remove uncertainty…more details
Yotam is a machine learning and deep learning expert with extensive hands-on experience in neural network development. Prior to co-founding Tensorleap, Yotam developed and led AI and Big Data projects from research to production for companies in the automotive and other sectors, as well as developing machine learning algorithms for large government projects, including the Soreq Nuclear Research Center (Israel).
In-person | Demo Talk
Attend this session to learn: What an ML model registry is and what problems it solves What considerations to have when implementing a model registry Why a Git-based model registry will make both your MLOps and DevOps teams happy…more details
Dmitry Petrov is an ex-Data Scientist at Microsoft with Ph.D. in Computer Science and active open source contributor. He has written and open sourced the first version of DVC.org – machine learning workflow management tool. Also he implemented Wavelet-based image hashing algorithm (wHash) in open source library ImageHash for Python. Now Dmitry is working on tools for machine learning and ML workflow management as a co-founder and CEO of Iterative in San Francisco.
In-person | Demo Talk
1. DataOps – a multi-stage problem
2. Solution Vectors – ways to reduce friction in the DataOps process
3. Ground Control
a. Today – what it is & how it works
b. Tomorrow – our roadmap
4. Q & A
Lucas is the Product Manager for Ground Control, iMerit’s single source of truth platform for managing data annotation workflows through reporting, analytics, and insights. Prior to iMerit, he designed and launched mapping technology for self-driving cars and developed electronics systems for high-performance vehicles. When not working in the trenches of machine learning, either as an engineer or Product Manager, you can find Lucas experimenting with ML in a variety of side projects, like using computer vision to optimize human biomechanics.
In-person | Demo Talk
Getting the full benefit of a machine learning model can be difficult, and getting users to leverage and adopt it can be even more so. Although we can turn data into forecasts and insights, these reveal what’s happened in the past and what’s likely to happen next. This can still leave users asking the most important question: What should we do? For that, we need help from optimization to give business users the tools to take full advantage of our machine learning models…more details
Mr. Yurchisin has over ten years’ experience applying operations research, machine learning, statistics, and data visualization to improve decision making. Before joining Gurobi, Jerry (who also goes by Jerome) was a Senior Consultant at OnLocation, Inc. where he customized several linear programming models within the National Energy Modeling System (NEMS) to analyze implementing specific energy policies and utilizing new technologies.
Prior to OnLocation, Jerry was an Operations Research Analyst & Data Scientist at Booz Allen Hamilton for over seven years. There he formulated scheduling and staffing integer programming models for the US Coast Guard, as well as led a project to quantify the maritime risks of offshore energy installations with the Research & Development Center. Further, Jerry was the technical lead on several Coast Guard studies including Living Marine Resources and Maritime Domain Awareness, providing statistical analysis and building supervised and unsupervised machine learning models. He also performed statistical analyses, machine learning modeling, and data visualization for cyberspace directorates at DoD and DHS.
Jerry has several years of experience teaching a wide variety of college-level mathematics and statistics courses and has a passion for education. He also enjoys golfing, biking, and writing about sports from an analytics point of view. He lives in Alexandria, Virginia with his wife, son, and two dogs.
Jerry holds B.S., Ed. and M.S., Mathematics degrees from Ohio University and an M.S. in Operations Research and Statistics from The University of North Carolina at Chapel Hill.
Virtual | Demo Talk
1. DataOps – a multi-stage problem
2. Solution Vectors – ways to reduce friction in the DataOps process
3. Ground Control
a. Today – what it is & how it works
b. Tomorrow – our roadmap
4. Q & A
Lucas is the Product Manager for Ground Control, iMerit’s single source of truth platform for managing data annotation workflows through reporting, analytics, and insights. Prior to iMerit, he designed and launched mapping technology for self-driving cars and developed electronics systems for high-performance vehicles. When not working in the trenches of machine learning, either as an engineer or Product Manager, you can find Lucas experimenting with ML in a variety of side projects, like using computer vision to optimize human biomechanics.
Virtual | Demo Talk | All Levels
Wouldn’t it be great if Python pandas worked with real-time, dynamic data? Or if publishing updating, derived data to apps and dashboards was easy? Deephaven Data Labs has an open solution exists today. Deephaven is a general-purpose data system built from the ground up to make working with real-time data easy — on its own or in combination with large batch loads…more details
Pete spent more than two decades on Wall Street, growing, and running automated trading groups. In 2005, he was the founding CEO of Walleye Capital, a multi-billion-dollar quant fund that derives value at the intersection of real-time data and automated applications. In 2017, Pete and some engineers spun a proprietary data engine out of Walleye, forming an independent company called Deephaven Data Labs. Deephaven is an open-first software shop, delivering a real-time query engine, APIs, UIs, and integrations to the community via open projects designed for diverse teams. Deephaven complements streaming technologies and makes dynamic data easy and accessible.
In-person | Demo Talk
You invest so much in your data infrastructure – you simply can’t afford to settle for broken pipelines and stale dashboards. At Monte Carlo, we believe in a world where you sleep soundly at night knowing you can trust your data. On November 1st, we’ll be hosting a live product demo with Bryce Heltzel, Sales Engineer at Monte Carlo, to share exactly how our product delivers end-to-end data observability across your data pipelines, from ingestion in the warehouse or lake to ETL and analytics…more details
In-person | Demo Talk
Graphs are everywhere, even in your existing data!
Join us for a demo of Neo4j’s enterprise-ready graph data platform and see firsthand how easy it is to start using the world’s most widely deployed graph database…more details
As a member of the Neo4j Field Engineering team, Stuart brings 15 years of experience helping many Global 2000 organizations solve their business challenges leveraging semantic technologies, natural language processing, search and graphs. In addition, he has experience across a wide range of industries, including healthcare, finance, manufacturing and retail. Based in the Bay Area, Stuart works with large enterprise companies including Wells Fargo, eBay, Visa, Adobe, Genentech, Kaiser and Cisco.
In-person | Demo Talk
Current Linux file system utilities like CoreUtil are based on software that’s been around for over 30 years. While compute, storage and networking technologies have revolutionized over that time, the most basic tools used by data scientists to manage, copy, index, and analyze data have largely remained the same. Learn how you can accelerate and simplify data pipelines and workflows associated with file management by 20X or more with Pure Storage and the Rapid File Toolkit 2.0…more details
Justin Emerson is a Principal Technology Evangelist at Pure Storage focused on the FlashBlade product portfolio. He joined Pure in 2020 as a FlashBlade Data Architect for the San Francisco Bay Area. Prior to that, he worked at storage-focused reseller partners for more than a decade.
In-person | Demo Talk
In this session, Cloudera will demonstrate how an AMP can be used for structural time series analysis. An Auto ML approach will be employed to forecast future cryptocurrency prices. To facilitate easy application usage, a Web-based, RESTful endpoint will be exposed to retrieve model predictions…more details
Jake is currently working as a Senior Product Marketing Manager over ML Lifecycle products at Cloudera. Before joining Cloudera, Jake worked as a Data Scientist and Solution Architect at ExxonMobil. Additionally, he worked as a Senior Data Scientist at FarmersEdge. Before starting his professional career, Jake obtained his bachelor’s and master’s degree from Brigham Young University. When he isn’t working, Jake enjoys skiing, golfing, and spending time with his family in the mountains.
Virtual | Demo Talk | All Levels
In this talk, we’ll explore the ways a time series platform supports data scientists. We’ll learn how you could use Telegraf open source collection agent to perform forecasting at the edge. We’ll explore how you can use Flux query language to prepare and clean your data as well as some preliminary data analysis. Next, we’ll learn about integrations with Jupyter and Zeppelin notebooks. Finally, we’ll cover some statistical properties of time series and some general recommendations for forecasting and anomaly detection algorithm selections…more details
Zoe Steinkamp is a developer Advocate for influxData. She was a front end software engineer for over 6 years before she moved into a developer advocate role. She has been with InfluxDB for over 3 years and she looks forward to sharing her knowledge of the platform and databases. She enjoys learning about awesome new technologies and doing at home tech projects to help make her life as well as other people’s lives easier. Her passions besides new technology include traveling and gardening.
In-person | Demo Talk | MLOps & Data Engineering | All Levels
In this session, we will show you how to accelerate MLOps using an example of a retail coupon application we recently built. We’ll discuss how data scientists build, test, and train ML models on Kubernetes hybrid cloud platform such as Red Hat OpenShift…more details
Audrey Reznik has been in the IT industry (private and public sectors) for 27 years in multiple verticals. In the last 4 years, she worked as a Data Scientist at ExxonMobil where she created a Data Science Enablement team to help data scientists easily deploy ML models in a Hybrid Cloud environment. Audrey was instrumental in educating scientists about what the OpenShift platform was and how to use OpenShift containers (images) to organize, run, and visualize data analysis results. Audrey now works as a Data Scientist with the Red Hat Data Science Team where she is focused on next-generation applications. She is passionate about Data Science and in particular the current opportunities with ML and Federated Data.
Virtual | Demo Talk | All Levels
In this session, we will present how open source powers companies’ approach to building a modern data stack. We will talk about technologies like Airbyte, Airflow, dbt, Preset, and how to connect them in order to build a customized and extensible data platform…more details
John is a Data Architect at Airbyte where he enjoys helping companies move data from where it’s created to where they want it to live. Before AIrbyte he worked as a Global Solutions Architect at LiveRamp where he helped companies activate data to transform customer experiences. Besides being in the weeds about data, John is an avid bike rider and golfer.
Virtual | Demo Talk | MLOps and Data Engineering | All Levels
A data science platform is an integrated set of tools that deliver the capabilities that most data science teams need. These capabilities are:
-The ability to do exploratory data analysis and create machine learning models.
-The ability to deploy models as APIs for other teams to use.
-The ability to schedule jobs and data pipelines to keep the business running.
-The ability to deploy dashboards for executives and stakeholders to view at any time.
-The ability to collaborate between members of the team easily on their work..more details
Hugo Shi a data science leader with 15 years of experience with data science and software projects at companies ranging from JP Morgan to the Chicago Trading Company. He is the CTO and co-founder of Saturn Cloud where he helps to make sure that Saturn Cloud is secure, scalable and easy to use for all data science teams. Hugo has a PhD in Signal Processing and his academic research focused on iterative reconstruction algorithms in medical imaging.
In-Person | Demo Talk | All Levels
A case study of efficiently solving a real-world computer vision problem using a combination of labelled real-world data and synthetic data, combining the strengths of each data type. It considers best practices for combining the datasets and showcases the benefits of a platform approach using Appen's platform for real world sourcing and labelling and the Mindtech Chameleon platform to generate the synthetic data…more details
Aaron is our Director of Solutions Engineering at Appen. He works closely with the Sales and Solutions teams to manage Fortune 500 deals through the pipeline. Aaron has lived in 7 cities around the world and is a geek at heart. He loves solving problems, breaking new technologies and identifying opportunities where technology can have a real impact on how we get things done.
Peter is VP of Engineering at Mindtech. Peter has many years of experience in semiconductors, with expertise in AI, GPU and VR/AR. Working at companies including Highwai, Imagination Technologies and ST. Peter has also been highly active in Khronos, including chairing the NNEF working group.
In-Person | Demo Talk | All Levels
In the Python open-source eco-system, many packages are available that cater to: – the building of great algorithms – the visualization of data – back-end functions Despite this, over 85% of Data Science Pilots remain pilots and do not make it to the production stage. With Taipy, Data Scientists/Python Developers will be able to build great pilots as well as stunning production-ready applications for end-users…more details
Vincent has 30+ years as AI specialist with ILOG and IBM. He has mentored several Data Science teams. Vincent has designed/modeled several major AI projects for customers such as Samsung. Electronics, McDonald’s, Dassault Aviation, Carhartt, Toyota, TSMC, Disney, etc. He is skilled in Mathematical Modeling, Machine Learning, Time Series prediction. He has strong experience in Manufacturing, Retail & Logistics industries. His main objective is to “Help companies go beyond AI pilots and be successful in bringing AI to their end-users”. He received his Msc in Comp. Science & AI from Paris-Saclay University.
Martin has over 30 years of experience in Data Science, AI, Decision Optimization. He worked as Consulting Project Manager, Technical Sales, Data Scientist with organizations including ILOG, IBM, Manhattan Associates, Emptoris. He has strong modeling skills in constraint programming, mathematical programming, machine learning. He is skilled in C++, Java, Python. Martin’s main objective is to help organizations identify and deploy analytics that maximize ROI. He was selected as INFORMS Franz Edelman Award finalist. He has studied M.S. in Operations Research from Massachusetts Institute of Technology.
Florian Jacta is a specialist of Taipy, a low-code open-source Python package enabling any Python developers to easily develop a production-ready AI application. Package pre-sales and after-sales functions. He is data Scientist for Groupe Les Mousquetaires (Intermarche) and ATOS. He developed several Predictive Models as part of strategic AI projects. Also, Florian got his master’s degree in Applied Mathematics from INSA, Major in Data Science and Mathematical Optimization.
In-person | Demo Talk | All Levels
– Why data teams require an observability platform designed specifically for data applications
– What multidimensional DataOps observability helps manage (performance, cost, data quality)
– How DataOps observability isn’t just for engineers or operations but empowers different data team members across the DataOps lifecycle…more details
Mike Wong is a Solutions Engineer at Unravel Data helping customers navigate the challenges of the modern data economy and optimize complex data stack. Previously, he spent nearly 20 years as a solution architect in a range of technology roles from PLM to Hadoop. His robust experience in the DataOps domain allows Mike to help customers achieve their vision with data applications and infrastructure.
Virtual | Demo Talk
Graphs are everywhere, even in your existing data!
Join us for a demo of Neo4j’s enterprise-ready graph data platform and see firsthand how easy it is to start using the world’s most widely deployed graph database…more details
As a member of the Neo4j Field Engineering team, Stuart brings 15 years of experience helping many Global 2000 organizations solve their business challenges leveraging semantic technologies, natural language processing, search and graphs. In addition, he has experience across a wide range of industries, including healthcare, finance, manufacturing and retail. Based in the Bay Area, Stuart works with large enterprise companies including Wells Fargo, eBay, Visa, Adobe, Genentech, Kaiser and Cisco.
Virtual | Demo Talk
Current Linux file system utilities like CoreUtil are based on software that’s been around for over 30 years. While compute, storage and networking technologies have revolutionized over that time, the most basic tools used by data scientists to manage, copy, index, and analyze data have largely remained the same. Learn how you can accelerate and simplify data pipelines and workflows associated with file management by 20X or more with Pure Storage and the Rapid File Toolkit 2.0…more details
In-Person | Demo Talk | All Levels
Wouldn’t it be great if Python pandas worked with real-time, dynamic data? Or if publishing updating, derived data to apps and dashboards was easy? Deephaven Data Labs has an open solution exists today. Deephaven is a general-purpose data system built from the ground up to make working with real-time data easy — on its own or in combination with large batch loads…more details
Pete spent more than two decades on Wall Street, growing, and running automated trading groups. In 2005, he was the founding CEO of Walleye Capital, a multi-billion-dollar quant fund that derives value at the intersection of real-time data and automated applications. In 2017, Pete and some engineers spun a proprietary data engine out of Walleye, forming an independent company called Deephaven Data Labs. Deephaven is an open-first software shop, delivering a real-time query engine, APIs, UIs, and integrations to the community via open projects designed for diverse teams. Deephaven complements streaming technologies and makes dynamic data easy and accessible.
In-person | Demo Talk
In this session I will be doing a live demonstration of how a few lines of code can make your machine learning workflows dramatically more observable, reproducible, and scalable…more details
Ben is a machine learning solutions consultant with W&B. He trains our customers to use W&B and works with them to improve their machine learning workflow. Prior to joining W&B he was training models and developing ml infrastructure for Samsung Research.
In-person | Demo Talk | MLOps and Data Engineering | All Levels
In this talk, we’ll showcase, through ML monitoring and notebooks, how data scientists and ML engineers can leverage ML monitoring to find the best data and retraining strategy mix to resolve machine learning performance issues. This data-driven, production-first approach enables more thoughtful retraining selections, shorter and leaner retraining cycles, and can be integrated into MLOps CI/CD pipelines for continuous model retraining upon anomaly detection…more details
Oryan is a ֿLead Software Engineer with a passion for Machine Learning and DevOps, with 7 years of experience developing services for production and development environments and leading teams.
Virtual | Demo Talk
Learn why the truly open source HPCC Systems platform is better at Big Data and offers an end-to-end solution for Developers and Data Scientists. Learn how ECL can empower you to build powerful data queries with ease. HPCC Systems, a comprehensive and dedicated data lake platform makes combining different types of data easier and faster than competing platforms — even data stored in massive, mixed schema data lakes — and it scales very quickly as your data needs grow. Topics include HPCC Architecture, Embedded Languages and external datastores, Machine Learning Library, Visualization, Application Security and more…more details
Bob has worked with the HPCC Systems technology platform and the ECL programming language for over a decade and has been a technical trainer for over 30 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote based training.
In-person| Demo Talk
Learn why the truly open source HPCC Systems platform is better at Big Data and offers an end-to-end solution for Developers and Data Scientists. Learn how ECL can empower you to build powerful data queries with ease. HPCC Systems, a comprehensive and dedicated data lake platform makes combining different types of data easier and faster than competing platforms — even data stored in massive, mixed schema data lakes — and it scales very quickly as your data needs grow. Topics include HPCC Architecture, Embedded Languages and external datastores, Machine Learning Library, Visualization, Application Security and more…more details
Bob has worked with the HPCC Systems technology platform and the ECL programming language for over a decade and has been a technical trainer for over 30 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote based training.
In-person | Demo Talk | MLOps and Data Engineering | All Levels
A data science platform is an integrated set of tools that deliver the capabilities that most data science teams need. These capabilities are:
-The ability to do exploratory data analysis and create machine learning models.
-The ability to deploy models as APIs for other teams to use.
-The ability to schedule jobs and data pipelines to keep the business running.
-The ability to deploy dashboards for executives and stakeholders to view at any time.
-The ability to collaborate between members of the team easily on their work..more details
Hugo Shi a data science leader with 15 years of experience with data science and software projects at companies ranging from JP Morgan to the Chicago Trading Company. He is the CTO and co-founder of Saturn Cloud where he helps to make sure that Saturn Cloud is secure, scalable and easy to use for all data science teams. Hugo has a PhD in Signal Processing and his academic research focused on iterative reconstruction algorithms in medical imaging.
In-person | Demo Talk
This demo works to demonstrate how a data scientist can take local environments, move it to a reproducible, scalable, and shareable compute environment, and hand off their results to the business and/or engineering personas. This standardization of hand-offs will help enable the creativity of the data science process by offloading the complexity ( and by extension cognitive load) of sharing data science work with organizations…more details
Chase is a solutions architect at Arrikto with a passion for connecting people to technical solutions that can prevent them from wasting precious time and mental energy- solving the same problems over and over. Chase is a certified Kubernetes Administrator, Developer, and Security Specialist who works to help clients reduce MLOps friction and toil while ensuring the “non-negotiables” are enforced to provide the best return on their production models.
Souheil is the Head of Field Data Science at Arrikto where he helps build machine learning solutions for clients. Previously, Souheil worked at Freddie Mac and Capital One where he built models and machine learning platforms. Prior to becoming a data scientist, he spent 15 years in academia working on MRI and Brain Imaging. Souheil holds a BS and PhD in Physics from Yale and MIT respectively.
Virtual | Demo Talk | All Levels
Why data teams require an observability platform designed specifically for data applications
What multidimensional DataOps observability helps manage (performance, cost, data quality)
How DataOps observability isn’t just for engineers or operations but empowers different data team members across the DataOps lifecycle..more details
In-person | Demo Talk | All Levels
In this session, we will demonstrate how AutoML for Images can be used to create a computer vision model from your image data. You will also learn about the various advanced capabilities in AutoML like small object detection, incremental training, big data support using streaming and multi-gpu/multi-node training…more details
Radu is an engineering manager in Azure Machine Learning at Microsoft, where he works on AI infrastructure for Deep Learning. Most recently, he has been leading the team that develops Azure AutoML’s computer vision capabilities – PyTorch deep learning models for image classification, object detection and segmentation. Prior to this, he designed and led the implementation of HyperDrive, Azure ML’s distributed hyperparameter tuning system. In previous roles, Radu has worked on different projects, ranging from search engine infrastructure to information retrieval and data mining. He holds a PhD in compilers and programing language design from INRIA Nancy, France.
Phani is a Senior Software Engineer at Microsoft. He has been working with Azure Machine Learning team for the past 5 years working on services for Hyperparameter tuning and Automated Machine Learning.
In-person | Demo Talk | MLOps and Data Engineering | All Levels
In this session, MLOps Architect Danny D. Leybzon will introduce the audience to the cutting edge Data+AI Observability platform WhyLabs. With WhyLabs, users can not only monitor their models’ performance in production, but also gain observability into the ML system, enabling them to improve the performance of deployed models. By understanding both theoretical and hands-on explanations of monitoring and observability, the audience will come away having learned about how to ensure that models in production stay performant…more details
Danny D. Leybzon has worn many hats, all of them related to data. He studied computational statistics at UCLA and has worked in the data and ML space ever since. In his role as MLOps architect, he has worked to evangelize machine learning best practices, talking on subjects such as distributed deep learning, productionizing machine learning models, automated machine learning, and lately has been talking about AI observability and data logging. When Danny’s not researching, practicing, or talking about data science, he’s usually doing one of his numerous outside hobbies: rock climbing, backcountry backpacking, skiing, etc.
In-Person | Demo Talk | All Levels
Hand labeling, a fundamental part of human-mediated machine intelligence in today’s age, is akin to scribes hand-copying books post-Gutenberg. What’s more is that the process is naive, dangerous, and expensive in light of the ever-growing world of alternatives which includes semi-supervised learning, weak supervision, and active learning…more details
Shayan Mohanty is the CEO and Co-Founder of Watchful, a company that largely automates the process of creating labeled training data. He’s spent over a decade of leading data engineering teams at various companies including Facebook, where he served as lead for the stream processing team responsible for processing 100% of the ads metrics data for all FB products. He is also a Guest Scientist at Los Alamos National Laboratory and has given talks on topics ranging from Automata Theory to Machine Teaching.
In-person | Demo Talk | All Levels
In this session, we will present how open source powers companies’ approach to building a modern data stack. We will talk about technologies like Airbyte, Airflow, dbt, Preset, and how to connect them in order to build a customized and extensible data platform…more details
John is a Data Architect at Airbyte where he enjoys helping companies move data from where it’s created to where they want it to live. Before AIrbyte he worked as a Global Solutions Architect at LiveRamp where he helped companies activate data to transform customer experiences. Besides being in the weeds about data, John is an avid bike rider and golfer.
In-person | Demo Talk
One must take a holistic view of the entire data analytics realm when it comes to planning for data science initiatives. Data engineering is a key enabler of data science, helping furnish reliable, quality data in a timely fashion. Delta Lake, an open-source storage layer that brings reliability to data lakes, can help take your data reliability to the next level. MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry…more details
Eric Vogelpohl is the Managing Director of Tech Strategy at Blueprint. He’s a proven IT professional with more than 20 years of experience and a high degree of technical and business acumen. He has an insatiable passion for all-things-tech, pro-cloud/SaaS, leadership, learning, and sharing ideas on how technology can turn data into information & transform user experiences.
In-person | Demo Talk | All Levels
When putting models into production it’s critical to know how they’re performing over time. As the last mile of the data pipeline, models can be impacted by a variety of issues, often outside the control of the data science team. “Observability” promises to help teams detect and prevent issues that could impact their models—but what is observability vs. data observability vs. ML observability? Get practical answers and recommendations from Kyle Kirwan, former product leader for Uber’s metadata tools, and founder of data observability company, Bigeye…more details
Kyle Kirwan is the co-founder and CEO of Bigeye, the data observability company. Before starting Bigeye, Kyle led the development of Uber’s internal data operations tools: a data catalog, data lineage collector, data pipeline testing, and incident management tools. He enjoys hiking and tiki bars.
Virtual | Keynote | NLP | All Levels
As natural language processing now permeates many different applications, its practical use is unquestionable. However, at the same time NLP is still imperfect, and errors cause everything from minor inconveniences to major PR disasters. Better understanding when our NLP models work and when they fail is critical to the efficient and reliable use of NLP in real-world scenarios. So how can we do so? In this talk I will discuss two issues: automatic evaluation of generated text, and automatic fine-grained analysis of NLP system results, which are some first steps towards a science of NLP model evaluation…more details
Graham Neubig is an associate professor at the Language Technologies Institute of Carnegie Mellon University. His research focuses on multilingual natural language processing, natural language interfaces to computers, and machine learning methods for NLP, with the final goal of every person in the world being able to communicate with each-other, and with computers in their own language. He also contributes to making NLP research more accessible through open publishing of research papers, advanced NLP course materials and video lectures, and open-source software, all of which are available on his web site.
Virtual | Keynote
Session Abstract Coming Soon!
Jun Zeng is HP’s Distinguished Technologist and founding manager of the 3D Digital Twin group. Jun has 20 years of industrial experiences in creating and commercializing software for improving cyber-physical systems. His publications include a co-edited book on computer-aided Design and a co-authored book on digital factory, and 50+ peer-reviewed papers. He has 58 U.S. patents granted and more pending. His academic training includes Ph.D. in mechanical engineering and M.S. in computer science, both from Johns Hopkins University. He is ACM member, and IEEE senior member.
In-person | Keynote | Machine Learning | Deep Learning | All Levels
Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Managing such sharing is one of the classical goals of microeconomics, and it is given new relevance in the modern setting of large, human-focused datasets, and in data-analytic contexts such as classifiers and recommendation systems…more details
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, and biological sciences; in recent years, he has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines, and applications to problems in distributed computing systems, natural language processing, signal processing, and statistical genetics. Previously, he was a professor at MIT. Michael is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science, the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA, and SIAM. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. Michael holds a master’s degree in mathematics from Arizona State University and a PhD in cognitive science from the University of California, San Diego.
In-person | Keynote | Machine Learning | Deep Learning | All Levels
Over the past decade the computation demands of machine learning (ML) workloads have grown much faster than the capabilities of a single processor, including hardware accelerators such as GPUs and TPUs. As a result researchers and practitioners have been left with no choice but to distribute these workloads. Unfortunately, developing distributed applications is very challenging. In this talk I will present two projects we developed at UC Berkeley, Ray (https://github.com/ray-project/ray) and Alpa (https://github.com/alpa-projects/alpa), that dramatically simplify scaling ML workloads..more details
Ion Stoica is a Professor in the EECS Department at University of California at Berkeley. He does research on cloud computing and networked computer systems. Past work includes Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an ACM Fellow and has received numerous awards, including the SIGOPS Hall of Fame Award (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001). In 2013, he co-founded Databricks a startup to commercialize technologies for Big Data processing, and in 2006 he co-founded and Conviva, a startup to commercialize technologies for large scale video distribution.
Virtual | Keynote | Responsible AI and Social Good
AI is ever more ubiquitous in our lives but all countries are not created equal in their access to or use of AI. Likewise all countries and businesses do not adhere to the same regulatory frameworks or opinions on governance. Yet all companies would benefit from knowing where they stand so that investment in technology is not ultimately wasted. Likewise, access to AI is being used as a geopolitical tool. What lessons are we able to draw and adopt now and how might this thinking mature into the future…more details
Kay Firth-Butterfield is Head of Artificial Intelligence and a member of the Executive Committee at the World Economic Forum and is one of the foremost experts in the world on the governance of AI. She is a Barrister, former Judge and Professor, technologist and entrepreneur who has an abiding interest in how humanity can equitably benefit from new technologies, especially AI. Kay is an Associate Barrister (Doughty Street Chambers), Master of the Inner Temple, London and serves on the Lord Chief Justice’s Advisory Panel on AI and Law. She co-founded AI Global and was the world’s first Chief AI Ethics officer in 2014 and created the AIEthics twitter hashtag. Kay is Vice-Chair of The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems and was part of the group which met at Asilomar to create the Asilomar AI Ethical Principles. She is on the Polaris Council for the Government Accountability Office (USA), the Advisory Board for UNESCO International Research Centre on AI and AI4All. Kay has advanced degrees in Law and International Relations and regularly speaks to international audiences addressing many aspects of the beneficial and challenging technical, economic and social changes arising from the use of AI. She has been consistently recognized as a leading woman in AI since 2018 and was featured in the New York Times as one of 10 Women Changing the Landscape of Leadership.
In-person | Keynote | Machine Learning | Deep Learning | All Levels
In this session, we will dive deep into Feathr, taking you on a journey into this scalable open-source feature store which has now joined the Linux Foundation AI and Data ecosystem. Feathr has been battle-tested in LinkedIn powering high scale ML applications, supporting 100s of training and inferencing pipelines. This enables feature sharing among teams, leading to significant business metrics gain…more details
Dr. Inchiosa’s passion for AI drives his work as Principal Data Scientist Manager in Azure Data’s Advanced Workload Engineering team, where he leads a team of data scientists focused on AI-led co-innovation engagements with strategic customers and partners. Previously, Mario served as Revolution Analytics’ Chief Scientist and as Analytics Architect in IBM’s Big Data organization, where he worked on advanced analytics in Hadoop, Teradata, and R. Prior to that, Mario was US Chief Scientist in Netezza Labs, bringing advanced analytics and R integration to Netezza’s SQL-based data warehouse appliances. He also served as US Chief Science Officer at NuTech Solutions, a computer science consultancy specializing in simulation, optimization, and data mining, and Senior Scientist at BiosGroup, a complexity science spin-off of the Santa Fe Institute. Mario holds Bachelor’s, Master’s, and PhD degrees in Physics from Harvard University. He has been awarded four patents and has published over 30 research papers, earning Publication of the Year and Open Literature Publication Excellence awards.
Virtual | Keynote | Machine Learning | Deep Learning | All Levels
In this session, we will dive deep into Feathr, taking you on a journey into this scalable open-source feature store which has now joined the Linux Foundation AI and Data ecosystem. Feathr has been battle-tested in LinkedIn powering high scale ML applications, supporting 100s of training and inferencing pipelines. This enables feature sharing among teams, leading to significant business metrics gain…more details
Dr. Inchiosa’s passion for AI drives his work as Principal Data Scientist Manager in Azure Data’s Advanced Workload Engineering team, where he leads a team of data scientists focused on AI-led co-innovation engagements with strategic customers and partners. Previously, Mario served as Revolution Analytics’ Chief Scientist and as Analytics Architect in IBM’s Big Data organization, where he worked on advanced analytics in Hadoop, Teradata, and R. Prior to that, Mario was US Chief Scientist in Netezza Labs, bringing advanced analytics and R integration to Netezza’s SQL-based data warehouse appliances. He also served as US Chief Science Officer at NuTech Solutions, a computer science consultancy specializing in simulation, optimization, and data mining, and Senior Scientist at BiosGroup, a complexity science spin-off of the Santa Fe Institute. Mario holds Bachelor’s, Master’s, and PhD degrees in Physics from Harvard University. He has been awarded four patents and has published over 30 research papers, earning Publication of the Year and Open Literature Publication Excellence awards.
In-person | Keynote | NLP | Machine Learning | Deep Learning | All Levels
What if computers can truly converse with us in our native tongue? Computers will transform into effective, personalized assistants for everybody. Commercial chatbots today are notoriously brittle as they are hardcoded to handle a few possible choices of user inputs. Recently introduced large language neural models, such as GPT-3, are remarkably fluent, but they are prone to hallucinations, often producing incorrect statements. This talk describes how we can tame these neural models into robust, trustworthy, and cost-effective conversational agents across all industries and languages…more details
Monica Lam is a Professor in the Computer Science Department at Stanford University since 1988. She is the faculty director of the Open Virtual Assistant Lab (OVAL). She received a B.Sc. from University of British Columbia in 1980 and a Ph.D. in Computer Science from Carnegie Mellon University in 1987. Monica is a Member of the National Academy of Engineering and an ACM Fellow. She is a co-author of the popular text Compilers, Principles, Techniques, and Tools (2nd Edition), also known as the Dragon book. Professor Lam’s current research is on conversational virtual assistants with an emphasis on privacy protection. Her research uses deep learning to map task-oriented natural language dialogues into formal semantics, represented by a new executable programming language called ThingTalk. Her Almond virtual assistant, trained on open knowledge graphs and IoT API standards, can be easily customized to perform new tasks. She is leading an Open Virtual Assistant Initiative to create the largest, open, crowdsourced language semantics model to promote open access in all languages. Her decentralized Almond virtual assistant that supports fine-grain sharing with privacy has received Popular Science’s Best of What’s New Award in Security in 2019.
Prof. Lam is also an expert in compilers for high-performance machines. Her pioneering work of affine partitioning provides a unifying theory to the field of loop transformations for parallelism and locality. Her software pipelining algorithm is used in commercial systems for instruction level parallelism. Her research team created the first, widely adopted research compiler, SUIF. Her contributions in computer architecture include the CMU Warp Systolic Array and the Stanford DASH Distributed Memory Multiprocessor. She was on the founding team of Tensilica, now a part of Cadence.
She received an NSF Young Investigator award in 1992, the ACM Most Influential Programming Language Design and Implementation Paper Award in 2001, an ACM SIGSOFT Distinguished Paper Award in 2002, the ACM Programming Language Design and Implementation Best Paper Award in 2004, the ACM SIGARCH/SIGPLAN/SIGOPS ASPLOS Influential Paper Awards in two consecutive years, 2021 and 2022. She was the author of two of the papers in “20 Years of PLDI–a Selection (1979-1999)”, and one paper in the “25 Years of the International Symposia on Computer Architecture”. She received the University of British Columbia Computer Science 50th Anniversary Research Award in 2018.
In-person | Demo Talk | All Levels
In this talk, we’ll explore the ways a time series platform supports data scientists. We’ll learn how you could use Telegraf open source collection agent to perform forecasting at the edge. We’ll explore how you can use Flux query language to prepare and clean your data as well as some preliminary data analysis. Next, we’ll learn about integrations with Jupyter and Zeppelin notebooks. Finally, we’ll cover some statistical properties of time series and some general recommendations for forecasting and anomaly detection algorithm selections…more details
Zoe Steinkamp is a developer Advocate for influxData. She was a front end software engineer for over 6 years before she moved into a developer advocate role. She has been with InfluxDB for over 3 years and she looks forward to sharing her knowledge of the platform and databases. She enjoys learning about awesome new technologies and doing at home tech projects to help make her life as well as other people’s lives easier. Her passions besides new technology include traveling and gardening.
In-person | Demo Talk | All Levels
When every millisecond and decision matters in racing, having the right data when you need it is critical. Z by HP Data Science workstations, mobile workstations, displays, hardware and software solutions help Hendrick Motorsports analyze, model, and simulate data with greater speed and accuracy. From high-end workstations to stack management to remote solutions, HP Data Science compute solutions help Hendrick Motorsports find their edge, on and off the track…more details
Alex first joined HP as a program manager on the Advanced Compute Solutions OEM team, providing extended life hardware solutions to critical partners across industry. Now, Alex is the product manager for Data Science Workstations. Alex manages Data Science Hardware, operating systems, and the Z by HP Data Science Stack Manager. Prior to HP, Alex received his MBA from the University of Texas-Austin and served in the United States Army.
Steve is the Software Engineering Lead for HP’s Data Science Solutions Team. For two years he’s been curating HP’s Data Science Stack and building processes to ensure compatibility across HP workstations. He studied Computer Science at Colorado State University, and no matter the season – he tries his hardest to get lost in the Rocky Mountains.
In-person | Keynote
Session Abstract Coming Soon!
Jun Zeng is HP’s Distinguished Technologist and founding manager of the 3D Digital Twin group. Jun has 20 years of industrial experiences in creating and commercializing software for improving cyber-physical systems. His publications include a co-edited book on computer-aided Design and a co-authored book on digital factory, and 50+ peer-reviewed papers. He has 58 U.S. patents granted and more pending. His academic training includes Ph.D. in mechanical engineering and M.S. in computer science, both from Johns Hopkins University. He is ACM member, and IEEE senior member.
In-Person | Demo Talk | All Levels
MLRun is an open-source MLOps orchestration framework. It exists to accelerate the integration of AI/ML applications into existing business workflows. MLRun introduces Data Scientists to a simple Python SDK that transforms their code into a production-quality application. It does so by abstracting the many layers involved in the MLOps pipeline. Developers can build, test, and tune their work anywhere and leverage MLRun to integrate with other components of their business workflow…more details
Nick is a passionate machine learning, data science, and MLOps enthusiast with experience across multiple domains including fraud detection, natural language processing, computer vision, and data mining. Nick holds a BSc. in Cognitive Science with a specialization in ML and Neural Computation from University of California, San Diego. He is an AWS Certified Solutions Architect, and has earned certifications in Python, Pytorch, Apache Airflow, PySpark and other frameworks. Currently, Nick acts as pre-sales MLOps Engineer at Iguazio, where he specializes in helping enterprises create real-world impact with their data science initiatives, with expertise in deployments on AWS, GCP, and Azure as well as on-premise Kubernetes architecture. Nick speaks at global industry events and blogs about MLOps, data science and ML Engineering.
Virtual | Demo Talk | All Levels
Azure AI Powered Global Translator App allows for easy document translation via both language and audio preferences through various Microsoft platforms. This app is for everyone e.g., healthcare, government, defense & intelligence, education, public safety & justice, and utilities, etc., who have a need to engage with citizens more directly and deeply through cloud-based audiovisual translation services and also looking for easier ways to access the digital content…more details
Savita is Data & AI evangelist based out of San Francisco Bay Area, USA. Savita brings 15 years of experience as a Technology professional during which she worked on Microsoft platform architecting and developing applications, automating solutions and integrations across Azure, M365, Power Platform and Teams.
She believes AI is a game changing development in human history which can solve some of the most daunting challenges humanity faces today. This idea drives her to relentlessly engage with customers, educate them on the potential of AI, showcase practical use cases and ultimately get them excited and thinking about how they can use AI to solve their organization’s pressing challenges.
Virtual | Demo Talk | All Levels
Oracle MySQL HeatWave is a fully managed database service, powered by the integrated HeatWave in-memory query accelerator. It’s the only cloud database service that combines transactions, analytics, and machine learning services into one MySQL Database, delivering real-time, secure analytics without the complexity, latency, and cost of ETL duplication. HeatWave Machine Learning (HeatWave ML) fully automates the process to train a model, generate inferences and invoke explanations, all without extracting data or model out of the database. The user can use familiar SQL interfaces to invoke all the machine learning capabilities. HeatWave ML leverages Oracle AutoML which automates model generation by replacing complex and time-consuming tasks such as data preprocessing, algorithm selection, feature selection and hyperparameter optimization that a data scientist is otherwise expected to perform. With MySQL HeatWave ML, developers and data analysts can build, train, and explain machine learning models in a fully automated way—25X faster than Amazon Redshift ML at 1% of the cost…more details
Sandeep Agrawal leads the HeatWave Machine Learning (HeatWave ML) project within MySQL HeatWave. HeatWave ML is the product of years of research and advanced development, and aims to help both data scientists and non-data scientists quickly apply ML to a given problem. Prior to HeatWave, Sandeep led the Oracle AutoML project within Oracle labs, creating a state-of-the-art distributed AutoML engine. He is passionate about Machine Learning and Systems Architecture, and a project like HeatWave ML that combines the two is heaven for him. Prior to Oracle, he completed his PhD in Computer Science from Duke University in 2015.
Salil Pradhan is a Product Manager in MySQL HeatWave team. His interests include distributed data processing, machine learning, cloud computing, middleware technologies as well as application areas such as Marketing Automation and Supply Chain Management.
In-Person | Demo Talk | All Levels
Oracle MySQL HeatWave is a fully managed database service, powered by the integrated HeatWave in-memory query accelerator. It’s the only cloud database service that combines transactions, analytics, and machine learning services into one MySQL Database, delivering real-time, secure analytics without the complexity, latency, and cost of ETL duplication. HeatWave Machine Learning (HeatWave ML) fully automates the process to train a model, generate inferences and invoke explanations, all without extracting data or model out of the database. The user can use familiar SQL interfaces to invoke all the machine learning capabilities. HeatWave ML leverages Oracle AutoML which automates model generation by replacing complex and time-consuming tasks such as data preprocessing, algorithm selection, feature selection and hyperparameter optimization that a data scientist is otherwise expected to perform. With MySQL HeatWave ML, developers and data analysts can build, train, and explain machine learning models in a fully automated way—25X faster than Amazon Redshift ML at 1% of the cost…more details
Sandeep Agrawal leads the HeatWave Machine Learning (HeatWave ML) project within MySQL HeatWave. HeatWave ML is the product of years of research and advanced development, and aims to help both data scientists and non-data scientists quickly apply ML to a given problem. Prior to HeatWave, Sandeep led the Oracle AutoML project within Oracle labs, creating a state-of-the-art distributed AutoML engine. He is passionate about Machine Learning and Systems Architecture, and a project like HeatWave ML that combines the two is heaven for him. Prior to Oracle, he completed his PhD in Computer Science from Duke University in 2015.
Salil Pradhan is a Product Manager in MySQL HeatWave team. His interests include distributed data processing, machine learning, cloud computing, middleware technologies as well as application areas such as Marketing Automation and Supply Chain Management.
Virtual | Demo Talk | All Levels
When every millisecond and decision matters in racing, having the right data when you need it is critical. Z by HP Data Science workstations, mobile workstations, displays, hardware and software solutions help Hendrick Motorsports analyze, model, and simulate data with greater speed and accuracy. From high-end workstations to stack management to remote solutions, HP Data Science compute solutions help Hendrick Motorsports find their edge, on and off the track…more details
Alex first joined HP as a program manager on the Advanced Compute Solutions OEM team, providing extended life hardware solutions to critical partners across industry. Now, Alex is the product manager for Data Science Workstations. Alex manages Data Science Hardware, operating systems, and the Z by HP Data Science Stack Manager. Prior to HP, Alex received his MBA from the University of Texas-Austin and served in the United States Army.
Steve is the Software Engineering Lead for HP’s Data Science Solutions Team. For two years he’s been curating HP’s Data Science Stack and building processes to ensure compatibility across HP workstations. He studied Computer Science at Colorado State University, and no matter the season – he tries his hardest to get lost in the Rocky Mountains.
Virtual | Demo Talk | All Levels
In this session, you will learn about innovative applied explainability techniques that will allow you to overcome familiar challenges in neural network development, such as creating balanced datasets, testing, edge case detection, troubleshooting, and auditing your models. We will deep dive into specific use cases so you can learn how to apply these techniques to your own models to remove uncertainty…more details
Yotam is a machine learning and deep learning expert with extensive hands-on experience in neural network development. Prior to co-founding Tensorleap, Yotam developed and led AI and Big Data projects from research to production for companies in the automotive and other sectors, as well as developing machine learning algorithms for large government projects, including the Soreq Nuclear Research Center (Israel).
In-person | Demo Talk
Attend this session to learn: What an ML model registry is and what problems it solves What considerations to have when implementing a model registry Why a Git-based model registry will make both your MLOps and DevOps teams happy…more details
Dmitry Petrov is an ex-Data Scientist at Microsoft with Ph.D. in Computer Science and active open source contributor. He has written and open sourced the first version of DVC.org – machine learning workflow management tool. Also he implemented Wavelet-based image hashing algorithm (wHash) in open source library ImageHash for Python. Now Dmitry is working on tools for machine learning and ML workflow management as a co-founder and CEO of Iterative in San Francisco.
In-person | Demo Talk
1. DataOps – a multi-stage problem
2. Solution Vectors – ways to reduce friction in the DataOps process
3. Ground Control
a. Today – what it is & how it works
b. Tomorrow – our roadmap
4. Q & A
Lucas is the Product Manager for Ground Control, iMerit’s single source of truth platform for managing data annotation workflows through reporting, analytics, and insights. Prior to iMerit, he designed and launched mapping technology for self-driving cars and developed electronics systems for high-performance vehicles. When not working in the trenches of machine learning, either as an engineer or Product Manager, you can find Lucas experimenting with ML in a variety of side projects, like using computer vision to optimize human biomechanics.
In-person | Demo Talk
Getting the full benefit of a machine learning model can be difficult, and getting users to leverage and adopt it can be even more so. Although we can turn data into forecasts and insights, these reveal what’s happened in the past and what’s likely to happen next. This can still leave users asking the most important question: What should we do? For that, we need help from optimization to give business users the tools to take full advantage of our machine learning models…more details
Mr. Yurchisin has over ten years’ experience applying operations research, machine learning, statistics, and data visualization to improve decision making. Before joining Gurobi, Jerry (who also goes by Jerome) was a Senior Consultant at OnLocation, Inc. where he customized several linear programming models within the National Energy Modeling System (NEMS) to analyze implementing specific energy policies and utilizing new technologies.
Prior to OnLocation, Jerry was an Operations Research Analyst & Data Scientist at Booz Allen Hamilton for over seven years. There he formulated scheduling and staffing integer programming models for the US Coast Guard, as well as led a project to quantify the maritime risks of offshore energy installations with the Research & Development Center. Further, Jerry was the technical lead on several Coast Guard studies including Living Marine Resources and Maritime Domain Awareness, providing statistical analysis and building supervised and unsupervised machine learning models. He also performed statistical analyses, machine learning modeling, and data visualization for cyberspace directorates at DoD and DHS.
Jerry has several years of experience teaching a wide variety of college-level mathematics and statistics courses and has a passion for education. He also enjoys golfing, biking, and writing about sports from an analytics point of view. He lives in Alexandria, Virginia with his wife, son, and two dogs.
Jerry holds B.S., Ed. and M.S., Mathematics degrees from Ohio University and an M.S. in Operations Research and Statistics from The University of North Carolina at Chapel Hill.
Virtual | Demo Talk
1. DataOps – a multi-stage problem
2. Solution Vectors – ways to reduce friction in the DataOps process
3. Ground Control
a. Today – what it is & how it works
b. Tomorrow – our roadmap
4. Q & A
Lucas is the Product Manager for Ground Control, iMerit’s single source of truth platform for managing data annotation workflows through reporting, analytics, and insights. Prior to iMerit, he designed and launched mapping technology for self-driving cars and developed electronics systems for high-performance vehicles. When not working in the trenches of machine learning, either as an engineer or Product Manager, you can find Lucas experimenting with ML in a variety of side projects, like using computer vision to optimize human biomechanics.
Virtual | Demo Talk | All Levels
Wouldn’t it be great if Python pandas worked with real-time, dynamic data? Or if publishing updating, derived data to apps and dashboards was easy? Deephaven Data Labs has an open solution exists today. Deephaven is a general-purpose data system built from the ground up to make working with real-time data easy — on its own or in combination with large batch loads…more details
Pete spent more than two decades on Wall Street, growing, and running automated trading groups. In 2005, he was the founding CEO of Walleye Capital, a multi-billion-dollar quant fund that derives value at the intersection of real-time data and automated applications. In 2017, Pete and some engineers spun a proprietary data engine out of Walleye, forming an independent company called Deephaven Data Labs. Deephaven is an open-first software shop, delivering a real-time query engine, APIs, UIs, and integrations to the community via open projects designed for diverse teams. Deephaven complements streaming technologies and makes dynamic data easy and accessible.
In-person | Demo Talk
You invest so much in your data infrastructure – you simply can’t afford to settle for broken pipelines and stale dashboards. At Monte Carlo, we believe in a world where you sleep soundly at night knowing you can trust your data. On November 1st, we’ll be hosting a live product demo with Bryce Heltzel, Sales Engineer at Monte Carlo, to share exactly how our product delivers end-to-end data observability across your data pipelines, from ingestion in the warehouse or lake to ETL and analytics…more details
In-person | Demo Talk
Graphs are everywhere, even in your existing data!
Join us for a demo of Neo4j’s enterprise-ready graph data platform and see firsthand how easy it is to start using the world’s most widely deployed graph database…more details
As a member of the Neo4j Field Engineering team, Stuart brings 15 years of experience helping many Global 2000 organizations solve their business challenges leveraging semantic technologies, natural language processing, search and graphs. In addition, he has experience across a wide range of industries, including healthcare, finance, manufacturing and retail. Based in the Bay Area, Stuart works with large enterprise companies including Wells Fargo, eBay, Visa, Adobe, Genentech, Kaiser and Cisco.
In-person | Demo Talk
Current Linux file system utilities like CoreUtil are based on software that’s been around for over 30 years. While compute, storage and networking technologies have revolutionized over that time, the most basic tools used by data scientists to manage, copy, index, and analyze data have largely remained the same. Learn how you can accelerate and simplify data pipelines and workflows associated with file management by 20X or more with Pure Storage and the Rapid File Toolkit 2.0…more details
Justin Emerson is a Principal Technology Evangelist at Pure Storage focused on the FlashBlade product portfolio. He joined Pure in 2020 as a FlashBlade Data Architect for the San Francisco Bay Area. Prior to that, he worked at storage-focused reseller partners for more than a decade.
In-person | Demo Talk
In this session, Cloudera will demonstrate how an AMP can be used for structural time series analysis. An Auto ML approach will be employed to forecast future cryptocurrency prices. To facilitate easy application usage, a Web-based, RESTful endpoint will be exposed to retrieve model predictions…more details
Jake is currently working as a Senior Product Marketing Manager over ML Lifecycle products at Cloudera. Before joining Cloudera, Jake worked as a Data Scientist and Solution Architect at ExxonMobil. Additionally, he worked as a Senior Data Scientist at FarmersEdge. Before starting his professional career, Jake obtained his bachelor’s and master’s degree from Brigham Young University. When he isn’t working, Jake enjoys skiing, golfing, and spending time with his family in the mountains.
Virtual | Demo Talk | All Levels
In this talk, we’ll explore the ways a time series platform supports data scientists. We’ll learn how you could use Telegraf open source collection agent to perform forecasting at the edge. We’ll explore how you can use Flux query language to prepare and clean your data as well as some preliminary data analysis. Next, we’ll learn about integrations with Jupyter and Zeppelin notebooks. Finally, we’ll cover some statistical properties of time series and some general recommendations for forecasting and anomaly detection algorithm selections…more details
Zoe Steinkamp is a developer Advocate for influxData. She was a front end software engineer for over 6 years before she moved into a developer advocate role. She has been with InfluxDB for over 3 years and she looks forward to sharing her knowledge of the platform and databases. She enjoys learning about awesome new technologies and doing at home tech projects to help make her life as well as other people’s lives easier. Her passions besides new technology include traveling and gardening.
In-person | Demo Talk | MLOps & Data Engineering | All Levels
In this session, we will show you how to accelerate MLOps using an example of a retail coupon application we recently built. We’ll discuss how data scientists build, test, and train ML models on Kubernetes hybrid cloud platform such as Red Hat OpenShift…more details
Audrey Reznik has been in the IT industry (private and public sectors) for 27 years in multiple verticals. In the last 4 years, she worked as a Data Scientist at ExxonMobil where she created a Data Science Enablement team to help data scientists easily deploy ML models in a Hybrid Cloud environment. Audrey was instrumental in educating scientists about what the OpenShift platform was and how to use OpenShift containers (images) to organize, run, and visualize data analysis results. Audrey now works as a Data Scientist with the Red Hat Data Science Team where she is focused on next-generation applications. She is passionate about Data Science and in particular the current opportunities with ML and Federated Data.
Virtual | Demo Talk | All Levels
In this session, we will present how open source powers companies’ approach to building a modern data stack. We will talk about technologies like Airbyte, Airflow, dbt, Preset, and how to connect them in order to build a customized and extensible data platform…more details
John is a Data Architect at Airbyte where he enjoys helping companies move data from where it’s created to where they want it to live. Before AIrbyte he worked as a Global Solutions Architect at LiveRamp where he helped companies activate data to transform customer experiences. Besides being in the weeds about data, John is an avid bike rider and golfer.
Virtual | Demo Talk | MLOps and Data Engineering | All Levels
A data science platform is an integrated set of tools that deliver the capabilities that most data science teams need. These capabilities are:
-The ability to do exploratory data analysis and create machine learning models.
-The ability to deploy models as APIs for other teams to use.
-The ability to schedule jobs and data pipelines to keep the business running.
-The ability to deploy dashboards for executives and stakeholders to view at any time.
-The ability to collaborate between members of the team easily on their work..more details
Hugo Shi a data science leader with 15 years of experience with data science and software projects at companies ranging from JP Morgan to the Chicago Trading Company. He is the CTO and co-founder of Saturn Cloud where he helps to make sure that Saturn Cloud is secure, scalable and easy to use for all data science teams. Hugo has a PhD in Signal Processing and his academic research focused on iterative reconstruction algorithms in medical imaging.
In-Person | Demo Talk | All Levels
A case study of efficiently solving a real-world computer vision problem using a combination of labelled real-world data and synthetic data, combining the strengths of each data type. It considers best practices for combining the datasets and showcases the benefits of a platform approach using Appen's platform for real world sourcing and labelling and the Mindtech Chameleon platform to generate the synthetic data…more details
Aaron is our Director of Solutions Engineering at Appen. He works closely with the Sales and Solutions teams to manage Fortune 500 deals through the pipeline. Aaron has lived in 7 cities around the world and is a geek at heart. He loves solving problems, breaking new technologies and identifying opportunities where technology can have a real impact on how we get things done.
Peter is VP of Engineering at Mindtech. Peter has many years of experience in semiconductors, with expertise in AI, GPU and VR/AR. Working at companies including Highwai, Imagination Technologies and ST. Peter has also been highly active in Khronos, including chairing the NNEF working group.
In-Person | Demo Talk | All Levels
In the Python open-source eco-system, many packages are available that cater to: – the building of great algorithms – the visualization of data – back-end functions Despite this, over 85% of Data Science Pilots remain pilots and do not make it to the production stage. With Taipy, Data Scientists/Python Developers will be able to build great pilots as well as stunning production-ready applications for end-users…more details
Vincent has 30+ years as AI specialist with ILOG and IBM. He has mentored several Data Science teams. Vincent has designed/modeled several major AI projects for customers such as Samsung. Electronics, McDonald’s, Dassault Aviation, Carhartt, Toyota, TSMC, Disney, etc. He is skilled in Mathematical Modeling, Machine Learning, Time Series prediction. He has strong experience in Manufacturing, Retail & Logistics industries. His main objective is to “Help companies go beyond AI pilots and be successful in bringing AI to their end-users”. He received his Msc in Comp. Science & AI from Paris-Saclay University.
Martin has over 30 years of experience in Data Science, AI, Decision Optimization. He worked as Consulting Project Manager, Technical Sales, Data Scientist with organizations including ILOG, IBM, Manhattan Associates, Emptoris. He has strong modeling skills in constraint programming, mathematical programming, machine learning. He is skilled in C++, Java, Python. Martin’s main objective is to help organizations identify and deploy analytics that maximize ROI. He was selected as INFORMS Franz Edelman Award finalist. He has studied M.S. in Operations Research from Massachusetts Institute of Technology.
Florian Jacta is a specialist of Taipy, a low-code open-source Python package enabling any Python developers to easily develop a production-ready AI application. Package pre-sales and after-sales functions. He is data Scientist for Groupe Les Mousquetaires (Intermarche) and ATOS. He developed several Predictive Models as part of strategic AI projects. Also, Florian got his master’s degree in Applied Mathematics from INSA, Major in Data Science and Mathematical Optimization.
In-person | Demo Talk | All Levels
– Why data teams require an observability platform designed specifically for data applications
– What multidimensional DataOps observability helps manage (performance, cost, data quality)
– How DataOps observability isn’t just for engineers or operations but empowers different data team members across the DataOps lifecycle…more details
Mike Wong is a Solutions Engineer at Unravel Data helping customers navigate the challenges of the modern data economy and optimize complex data stack. Previously, he spent nearly 20 years as a solution architect in a range of technology roles from PLM to Hadoop. His robust experience in the DataOps domain allows Mike to help customers achieve their vision with data applications and infrastructure.
Virtual | Demo Talk
Graphs are everywhere, even in your existing data!
Join us for a demo of Neo4j’s enterprise-ready graph data platform and see firsthand how easy it is to start using the world’s most widely deployed graph database…more details
As a member of the Neo4j Field Engineering team, Stuart brings 15 years of experience helping many Global 2000 organizations solve their business challenges leveraging semantic technologies, natural language processing, search and graphs. In addition, he has experience across a wide range of industries, including healthcare, finance, manufacturing and retail. Based in the Bay Area, Stuart works with large enterprise companies including Wells Fargo, eBay, Visa, Adobe, Genentech, Kaiser and Cisco.
Virtual | Demo Talk
Current Linux file system utilities like CoreUtil are based on software that’s been around for over 30 years. While compute, storage and networking technologies have revolutionized over that time, the most basic tools used by data scientists to manage, copy, index, and analyze data have largely remained the same. Learn how you can accelerate and simplify data pipelines and workflows associated with file management by 20X or more with Pure Storage and the Rapid File Toolkit 2.0…more details
In-Person | Demo Talk | All Levels
Wouldn’t it be great if Python pandas worked with real-time, dynamic data? Or if publishing updating, derived data to apps and dashboards was easy? Deephaven Data Labs has an open solution exists today. Deephaven is a general-purpose data system built from the ground up to make working with real-time data easy — on its own or in combination with large batch loads…more details
Pete spent more than two decades on Wall Street, growing, and running automated trading groups. In 2005, he was the founding CEO of Walleye Capital, a multi-billion-dollar quant fund that derives value at the intersection of real-time data and automated applications. In 2017, Pete and some engineers spun a proprietary data engine out of Walleye, forming an independent company called Deephaven Data Labs. Deephaven is an open-first software shop, delivering a real-time query engine, APIs, UIs, and integrations to the community via open projects designed for diverse teams. Deephaven complements streaming technologies and makes dynamic data easy and accessible.
In-person | Demo Talk
In this session I will be doing a live demonstration of how a few lines of code can make your machine learning workflows dramatically more observable, reproducible, and scalable…more details
Ben is a machine learning solutions consultant with W&B. He trains our customers to use W&B and works with them to improve their machine learning workflow. Prior to joining W&B he was training models and developing ml infrastructure for Samsung Research.
In-person | Demo Talk | MLOps and Data Engineering | All Levels
In this talk, we’ll showcase, through ML monitoring and notebooks, how data scientists and ML engineers can leverage ML monitoring to find the best data and retraining strategy mix to resolve machine learning performance issues. This data-driven, production-first approach enables more thoughtful retraining selections, shorter and leaner retraining cycles, and can be integrated into MLOps CI/CD pipelines for continuous model retraining upon anomaly detection…more details
Oryan is a ֿLead Software Engineer with a passion for Machine Learning and DevOps, with 7 years of experience developing services for production and development environments and leading teams.
Virtual | Demo Talk
Learn why the truly open source HPCC Systems platform is better at Big Data and offers an end-to-end solution for Developers and Data Scientists. Learn how ECL can empower you to build powerful data queries with ease. HPCC Systems, a comprehensive and dedicated data lake platform makes combining different types of data easier and faster than competing platforms — even data stored in massive, mixed schema data lakes — and it scales very quickly as your data needs grow. Topics include HPCC Architecture, Embedded Languages and external datastores, Machine Learning Library, Visualization, Application Security and more…more details
Bob has worked with the HPCC Systems technology platform and the ECL programming language for over a decade and has been a technical trainer for over 30 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote based training.
In-person| Demo Talk
Learn why the truly open source HPCC Systems platform is better at Big Data and offers an end-to-end solution for Developers and Data Scientists. Learn how ECL can empower you to build powerful data queries with ease. HPCC Systems, a comprehensive and dedicated data lake platform makes combining different types of data easier and faster than competing platforms — even data stored in massive, mixed schema data lakes — and it scales very quickly as your data needs grow. Topics include HPCC Architecture, Embedded Languages and external datastores, Machine Learning Library, Visualization, Application Security and more…more details
Bob has worked with the HPCC Systems technology platform and the ECL programming language for over a decade and has been a technical trainer for over 30 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote based training.
In-person | Demo Talk | MLOps and Data Engineering | All Levels
A data science platform is an integrated set of tools that deliver the capabilities that most data science teams need. These capabilities are:
-The ability to do exploratory data analysis and create machine learning models.
-The ability to deploy models as APIs for other teams to use.
-The ability to schedule jobs and data pipelines to keep the business running.
-The ability to deploy dashboards for executives and stakeholders to view at any time.
-The ability to collaborate between members of the team easily on their work..more details
Hugo Shi a data science leader with 15 years of experience with data science and software projects at companies ranging from JP Morgan to the Chicago Trading Company. He is the CTO and co-founder of Saturn Cloud where he helps to make sure that Saturn Cloud is secure, scalable and easy to use for all data science teams. Hugo has a PhD in Signal Processing and his academic research focused on iterative reconstruction algorithms in medical imaging.
In-person | Demo Talk
This demo works to demonstrate how a data scientist can take local environments, move it to a reproducible, scalable, and shareable compute environment, and hand off their results to the business and/or engineering personas. This standardization of hand-offs will help enable the creativity of the data science process by offloading the complexity ( and by extension cognitive load) of sharing data science work with organizations…more details
Chase is a solutions architect at Arrikto with a passion for connecting people to technical solutions that can prevent them from wasting precious time and mental energy- solving the same problems over and over. Chase is a certified Kubernetes Administrator, Developer, and Security Specialist who works to help clients reduce MLOps friction and toil while ensuring the “non-negotiables” are enforced to provide the best return on their production models.
Souheil is the Head of Field Data Science at Arrikto where he helps build machine learning solutions for clients. Previously, Souheil worked at Freddie Mac and Capital One where he built models and machine learning platforms. Prior to becoming a data scientist, he spent 15 years in academia working on MRI and Brain Imaging. Souheil holds a BS and PhD in Physics from Yale and MIT respectively.
Virtual | Demo Talk | All Levels
Why data teams require an observability platform designed specifically for data applications
What multidimensional DataOps observability helps manage (performance, cost, data quality)
How DataOps observability isn’t just for engineers or operations but empowers different data team members across the DataOps lifecycle..more details
In-person | Demo Talk | All Levels
In this session, we will demonstrate how AutoML for Images can be used to create a computer vision model from your image data. You will also learn about the various advanced capabilities in AutoML like small object detection, incremental training, big data support using streaming and multi-gpu/multi-node training…more details
Radu is an engineering manager in Azure Machine Learning at Microsoft, where he works on AI infrastructure for Deep Learning. Most recently, he has been leading the team that develops Azure AutoML’s computer vision capabilities – PyTorch deep learning models for image classification, object detection and segmentation. Prior to this, he designed and led the implementation of HyperDrive, Azure ML’s distributed hyperparameter tuning system. In previous roles, Radu has worked on different projects, ranging from search engine infrastructure to information retrieval and data mining. He holds a PhD in compilers and programing language design from INRIA Nancy, France.
Phani is a Senior Software Engineer at Microsoft. He has been working with Azure Machine Learning team for the past 5 years working on services for Hyperparameter tuning and Automated Machine Learning.
In-person | Demo Talk | MLOps and Data Engineering | All Levels
In this session, MLOps Architect Danny D. Leybzon will introduce the audience to the cutting edge Data+AI Observability platform WhyLabs. With WhyLabs, users can not only monitor their models’ performance in production, but also gain observability into the ML system, enabling them to improve the performance of deployed models. By understanding both theoretical and hands-on explanations of monitoring and observability, the audience will come away having learned about how to ensure that models in production stay performant…more details
Danny D. Leybzon has worn many hats, all of them related to data. He studied computational statistics at UCLA and has worked in the data and ML space ever since. In his role as MLOps architect, he has worked to evangelize machine learning best practices, talking on subjects such as distributed deep learning, productionizing machine learning models, automated machine learning, and lately has been talking about AI observability and data logging. When Danny’s not researching, practicing, or talking about data science, he’s usually doing one of his numerous outside hobbies: rock climbing, backcountry backpacking, skiing, etc.
In-Person | Demo Talk | All Levels
Hand labeling, a fundamental part of human-mediated machine intelligence in today’s age, is akin to scribes hand-copying books post-Gutenberg. What’s more is that the process is naive, dangerous, and expensive in light of the ever-growing world of alternatives which includes semi-supervised learning, weak supervision, and active learning…more details
Shayan Mohanty is the CEO and Co-Founder of Watchful, a company that largely automates the process of creating labeled training data. He’s spent over a decade of leading data engineering teams at various companies including Facebook, where he served as lead for the stream processing team responsible for processing 100% of the ads metrics data for all FB products. He is also a Guest Scientist at Los Alamos National Laboratory and has given talks on topics ranging from Automata Theory to Machine Teaching.
In-person | Demo Talk | All Levels
In this session, we will present how open source powers companies’ approach to building a modern data stack. We will talk about technologies like Airbyte, Airflow, dbt, Preset, and how to connect them in order to build a customized and extensible data platform…more details
John is a Data Architect at Airbyte where he enjoys helping companies move data from where it’s created to where they want it to live. Before AIrbyte he worked as a Global Solutions Architect at LiveRamp where he helped companies activate data to transform customer experiences. Besides being in the weeds about data, John is an avid bike rider and golfer.
In-person | Demo Talk
One must take a holistic view of the entire data analytics realm when it comes to planning for data science initiatives. Data engineering is a key enabler of data science, helping furnish reliable, quality data in a timely fashion. Delta Lake, an open-source storage layer that brings reliability to data lakes, can help take your data reliability to the next level. MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry…more details
Eric Vogelpohl is the Managing Director of Tech Strategy at Blueprint. He’s a proven IT professional with more than 20 years of experience and a high degree of technical and business acumen. He has an insatiable passion for all-things-tech, pro-cloud/SaaS, leadership, learning, and sharing ideas on how technology can turn data into information & transform user experiences.
In-person | Demo Talk | All Levels
When putting models into production it’s critical to know how they’re performing over time. As the last mile of the data pipeline, models can be impacted by a variety of issues, often outside the control of the data science team. “Observability” promises to help teams detect and prevent issues that could impact their models—but what is observability vs. data observability vs. ML observability? Get practical answers and recommendations from Kyle Kirwan, former product leader for Uber’s metadata tools, and founder of data observability company, Bigeye…more details
Kyle Kirwan is the co-founder and CEO of Bigeye, the data observability company. Before starting Bigeye, Kyle led the development of Uber’s internal data operations tools: a data catalog, data lineage collector, data pipeline testing, and incident management tools. He enjoys hiking and tiki bars.
Schedule will be updated frequently. More sessions coming soon.
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