LIVE OCTOBER 29th
Applied AI Speakers
A FREE VIRTUAL EVENT
More Applied AI Instructors Coming Soon
Jen Underwood
Jen Underwood has a unique blend of product management and “hands-on” experience in data warehousing, reporting, visualization, and advanced analytics. In addition to keeping a constant pulse on industry trends, she enjoys digging into oceans of data to solve complex problems with machine learning.Over the past 20 years, Jen has held worldwide product management roles at Microsoft andserved as a technical lead for system implementation firms. She has experience launching new products and turning around failed projects. Most recently she provided advisory, strategy, educational content development, and marketing services to 100+ technology vendors through her own firm. She has been mentioned by KD Nuggets, Information Management and Forbes for her work. She also has written for InformationWeek, O’Reilly Media, and numerous other tech industry publications. Jen has a Bachelor of Business Administration – Marketing, Cum Laude from the University of Wisconsin, Milwaukee and a post-graduate certificate in Computer Science – Data Mining from the University of California, San Diego. She was also honored to be a former IBM Analytics Insider, Tableau Zen Master, and Top 10 Women Influencer.
Sean Smith
Sean Smith is a Director of Success at DataRobot where he supports customers development of actionable intelligence with automated machine learning. Sean has held multiple positions in advanced analytics & machine learning with some of today’s largest technology companies and is a contributing author to Towards AI, one of the top to artificial intelligence publications on Medium.
The AI Practitioner Series – Data Prep Walkthrough (A Reusable Framework!)
Ben Taylor, PhD
Ben Taylor has over 17 years of machine-learning experience. After studying chemical engineering, Taylor joined Intel and Micron and worked in their photolithography, process control, and yield prediction groups. Pursuing his love for high-performance computing (HPC) and predictive modeling, Taylor joined an artificial intelligence hedge fund (AIQ) as their AI expert. Taylor then joined a young HR startup called HireVue and built out their data science group and helped o launch HireVue’s AI insights product using video/audio from candidate interviews. In 2017 Taylor co-founded Zeff.ai to pursue deep learning for image, audio, video, and text for the enterprise.
Building & Selling AI Startups(Business Talk)
Jett Oristaglio
Jett Oristaglio is the Data Science and Product Lead of Trusted AI at DataRobot. He has a background in Cognitive Science, with focuses in computer vision, neuro-ethics, and transcendent states of consciousness. His primary mission at DataRobot is to answer the questions: “What is everything you need in order to trust a decision-making system with your life? And what tools can we build to automated that process as comprehensively as possible?
Jacqueline Amable
Jackie Amable currently serves as the Managing Director for Nextcorps new ClimateTech Studio. Nextcorps new ClimateTech Studio supports New York State’s bold vision to be leaders in ClimateTech adoption and environmental justice alongside our partners and co-creators NYSERDA and SecondMuse. Previously, she was the CEO & Founder of Revolar. At Revolar they built tiny devices to keep your loved ones safe. She is incredibly proud of the team and products they built during her tenure- they never missed a deadline. They raised millions from great investors such as Foundry Group and Techstars. With her role at Techstars, she covered the Americas and helped elevate entrepreneurs and ecosystem builders from Canada all the way to Argentina. She is an advisor to startups such as microTERRA and for businesses in manufacturing, sustainability, and supply chain space. From circular food systems, localized fashion supply chains, workforce tech, future city tech, and wearable tech, she is deeply committed to helping our global community evolve our systems. At her core, she is a teacher, researcher, doer, and proud nerd.
Fireside Chat with Jacqueline Ros Amable – AI in Climate Tech
Seph Mard
Seph Mard joins DataRobot as a recognized industry leader of enterprise model risk management, model validation, model governance and best practices. Seph has more than a decade of experience applying data science to quantitative finance and risk management. As Director of Technical Product, Seph is a leader on DataRobot Product Management team where he is focused exclusively on ML Ops product management and strategy. Seph is bringing innovation into the world of Machine Learning Operations using DataRobot’s superior machine learning automation and data science edge. He holds dual M.Sc. degrees in Applied Mathematics and Econometrics.
Kate Strachnyi
Kate Strachnyi is the Founder of Story by Data, the DATAcated Academy, and the DATAcated Conference. She’s an advisory board member for the Initiative for Analytics and Data Science Standards. Kate is also an author, data visualization specialist, and was named as a LinkedIn Top Voice in Data Science & Analytics in 2018 and 2019.
Shyam Ayyar
Shyam Ayyar is a Senior Product Manager at DataRobot, where his primary focus is data preparation. He’s worked at Paxata for 3 years focussing on Data Preparation. He has over a decade of experience in Data Management & Analytics primarily in the Financial Services industry with companies such as JP Morgan Chase where he has helped to build data pipelines. Shyam studied Computer Science and has worked in a variety of roles in building software products.
The AI Practitioner Series – Data Prep Walkthrough (A Reusable Framework!)
Ryan Sevey
Ryan Sevey is the general manager of developer experience at DataRobot. He has a background in everything from AI/ML to Cybersecurity and game development.
Fireside Chat with Jacqueline Ros Amable – AI in Climate Tech
Anton Kasyanov
Principle Machine Learning Engineer at DataRobot for 3+ years leads a team behind Visual AI. Anton connects R&D engineering team with product management to deliver the best product features that provide a lot of value to the customers. Over the course of his career, he worked on delivering various Machine Learning systems to the cloud. Anton has publications in the area of Computer Vision and got his Master’s degree from RWTH Aachen, Germany.
Susan Walsh
With nearly a decade of experience fixing your dirty data, Susan Walsh is The Classification Guru. She brings clarity and accuracy to data and procurement; helps teams work more effectively and efficiently; and cuts through the jargon to address the issues of dirty data and its consequences in an entertaining and engaging way. Susan is a specialist in data classification, supplier normalization, taxonomy customization, and data cleansing and can help your business find cost savings through spend and time management – supporting better, more informed business decisions. Susan has developed a methodology to accurately and efficiently classify, cleanse, and check data for errors which will help prevent costly mistakes and could save days, if not weeks of laborious cleansing and classifying. Susan is passionate about helping you find the value in cleaning your ‘dirty data’ and raises awareness of the consequences of ignoring issues through her blogs, vlogs, webinars, and speaking engagements.
Danny Ma
Danny is the Founder and CEO of Sydney Data Science and has over 10 years of experience in the data industry. He has held almost every role in the data ecosystem from data entry to campaign analyst, data scientist, data engineer and machine learning engineer. His core expertise is in data analytics, supervised ML algorithms, data architecture and designing digital data systems for retail, banking and financial markets. Danny’s passion is to guide businesses and individuals on their data & machine learning journey. He currently runs the Data With Danny community with over 8,000 aspiring data professionals and is working on his vision of creating a scalable virtual data apprenticeship program to empower others to kickstart their career in data.
SQL Masterclass for Data Scientists(Half-Day Training)
Tim Whittaker
David Gonzales
David “Gonzo” Gonzalez is a “reformed” Data Science professional with well over a decade of experience putting AI and ML into production in mission-critical systems. At DataRobot he is working to empower traditional software development teams comprised of product visionaries and engineers with tools and solutions that will allow them to more easily incorporate AI into what they build. Prior to DataRobot he worked on multiple auto-ml platforms and pioneered transactionally authored training and inference multi-modal datasets at Zeff.ai where he was CEO and co-founder. Gonzo is the primary author of The Manifesto for Applied Artificial Intelligence Development and has multiple ML patents. In his free time, he enjoys playing outdoors in his adoptive home of Utah with his wife and children.
Hands-on Data Science for Software Developers — A Live Coding Session with Data Robot Self-Service
Ivan Pyzow
Ivan Pyzow is a deep learning engineer at DataRobot on the Visual AI team, focused on implementing state-of-the-art techniques in features that are accessible to a wide range of data scientists. Ivan has worked as a data scientist and engineer at McMaster-Carr Supply and McKinsey & Company, where he built production pipelines for neural networks for search engines, fraud detection systems, and satellite monitoring for agriculture.
Dustin Burke
Since the start of my career 15 years ago, I’ve explored various aspects of how humans and machines can work together – AI / Machine Learning, human-computer interaction, semantic reasoning, etc. At first it was about the technology and how to advance its capability, but has shifted more towards the role of humans and how to maximize their performance within a complex system of automation and AI. As an early employee at DataRobot, I’ve had the opportunity to lead and grow many teams – Release, QA, Test Automation, Developer Experience, Developer Enablement and Engineering Productivity – and am passionate about enabling developers to succeed. I love coding, breaking things, and making things more usable.
Some Failures and Lessons Learned Using AI in our AI company
Borys Drozhak
Borys believes that the software development world could use an upgrade and that all engineers can boost their productivity beyond their current level if they start being scientific and pragmatic about their development processes. Borys and his team are pioneering an emerging software development discipline to discover how engineering can make R&D more productive and developers happier.
Some Failures and Lessons Learned Using AI in our AI Company
Ina Ko
Ina is focused on building the product strategy, vision, and mission for new capabilities that help business users and executives realize tangible value out of AI. Ina is an analyst-turned-product manager with over 11 years of experience building and commercializing capabilities in artificial intelligence and enterprise performance management.
Edward M. Young
Edward M. Young is the director of Advanced Analytics at FCA Fiat Chrysler. Although most of his career history is with accounting and finance positions for several leading companies, over time, Ed recognized the power that advanced technology could bring to business and he began to pursue his interest in predictive analytics. Today, Ed leads a team of data scientists on important business questions and is extending the reach of AI to 25 business analysts within his organization.
Eric Weber
Eric is currently the Head of Experimentation at Yelp. In that role, he focuses on scaling the experimentation platform to handle challenging product problems. He has held senior leadership roles and senior individual contributor roles at companies like LinkedIn and CoreLogic. Previously, he was a university professor at Oregon State University and University of Minnesota. He has a Ph.D. in mathematics, a Masters in Business Analytics and is currently completing an MBA at University of Chicago-Booth.
Experimentation, Metrics and Analytics: An Ecosystem for Data Informed Decisions
Michael Balint
Michael Balint is a Senior Product Manager at NVIDIA focused on cluster management, orchestration, and scheduling of NVIDIA DGX servers. Prior to working at NVIDIA, Michael was a White House Presidential Innovation Fellow, where he brought his technical expertise to projects like VP Biden’s Cancer Moonshot and Code.gov. A graduate of both Cornell and Johns Hopkins University, he has had the good fortune of applying software engineering and data science to many interesting problems throughout his career, including: optimization of air traffic flows for the FAA, NLP summarization of makeup reviews, and repurposing geospatial anomaly detection to the discovery of abnormal skin lesions.
Rajiv Shah, PhD
Rajiv Shah is a machine learning engineer at Hugging Face who focuses on enabling enterprise teams to succeed with AI. Rajiv is a leading expert in the practical application of AI. Previously, he led data science enablement efforts across hundreds of data scientists at DataRobot. He was also a part of data science teams at Snorkel AI, Caterpillar, and State Farm. Rajiv is a widely recognized speaker on AI, published over 20 research papers, and received over 20 patents, including sports analytics, deep learning, and interpretability. Rajiv holds a PhD in Communications and a Juris Doctor from the University of Illinois at Urbana Champaign. While earning his degrees, he received a fellowship in Digital Government from the John F. Kennedy School of Government at Harvard University. He also has a large following on AI-related short videos on Tik Tok and Instagram at @rajistics.
Evaluation Techniques for Large Language Models(Tutorial)