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)

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.

Data-centric AI: The Winning Combination of Human-in-the-loop Real-World Data and Synthetic Data for Edge Cases(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.

Unlocking High Quality Data with Ground Control(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.

Demo Talk Title: Building and Deploying a Gen AI App in 20 Minutes

Abstract:

Generative AI has captured the imagination of many, but building your own Gen AI application is no easy feat. In this session, we’ll demonstrate how you can fine-tune a Gen AI model, build a Gen AI application, and deploy it in 20 minutes. For this exercise, we will use the following open source tools:

a. MLRun – MLOps orchestration framework
b. Langchain – used for building LLM applications
c. Milvus – open-source vector store for indexing documents

We’ll touch upon issues like accelerating the integration of AI/ML applications into existing business workflows, leveraging simple Python SDKs that transform code into a production-quality application, abstracting the many layers involved in the MLOps pipeline, building, testing, and tuning your work anywhere while integrating with other components of their business workflow.

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)

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)

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.

A Unified and User Friendly Approach to Develop ML Solutions in MySQL HeatWave AutoML(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)

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)

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)