AI EXPO & DEMO HALL
AI Solutions Showcase & Networking
SAN FRANCISCO | OCT 31 – NOV 1
HYATT REGENCY, BURLINGAME CA

TALKS AND DEMOS
PARTNERS
NETWORKING EVENTS
ATTENDEES (in-person & Virtual)
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.

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)

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.

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)

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)

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 currently holds the position of Principal Technical Evangelist at Cloudera, where he promotes the strengths of Cloudera’s Lakehouse for delivering trusted AI. His tenure at Cloudera began as a Senior Product Marketing Manager for Cloudera Machine Learning (CML).
Before Cloudera, Jake developed his ML expertise at ExxonMobil, starting as a Data Scientist and later transitioning to a Data Science and Analytics Solution Architect role. He also contributed significantly at FarmersEdge, taking on responsibilities as a Senior Data Scientist and subsequently as a Data Science Manager.
Jake earned both his bachelor’s and master’s degrees from Brigham Young University in Information Systems Management with an emphasis in Statistics.
Outside of work, Jake is passionate about outdoor activities. He enjoys skiing, golfing, rafting, and hiking. However, spending time with his family amidst the mountains remains his most rewarding pastime.

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)

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)
Bringing AI to Retail and Fast Food with Taipy’s Applications(Track Keynote)
Demo 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:
1. Taipy-GUI goes way beyond the capabilities of the standard graphical stack: Gradio,
Streamlit, Dash, etc.
2. 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.
How to Build Stunning Data Science Web applications in Python – Taipy Tutorial(Tutorial)
Bringing AI to Retail and Fast Food with Taipy’s Applications(Track Keynote)

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 Guidera
Audrey is a Sr. Principal Software Engineer in the Red Hat Cloud Services – Red Hat OpenShift Data Science team focusing on helping customers with managed services, AI/ML workloads and next-generation platforms. She holds a degree in Computer Information Systems and has been working in the IT Industry for over 20 years in full stack development to data science roles. Audrey is passionate about Data Science and in particular the current opportunities with AIML at the Edge and Open Source technologies.
Data Science Software Acceleration at the Edge(AiX Keynote)

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)
Who Should Attend?
AI Expo & Demo Hall gathers executives, business professionals, experts, and data scientists transforming the enterprise with Artificial Intelligence.
Business Leaders and Executives: Chief Data Scientists, Chief AI Officers, CDO, CIO, CTO, VP of Engineering, R&D, Marketing, Business Development, Product, Development, Data
Directors of Data Science, Data Analytics Managers, Heads of Data and Innovation; Software, IT, and Product Managers
Data Science Professionals: Data Scientists, Data Engineers, Data Analysts, Architects, ML and DL Experts, Database Admins
Software Development Experts: Software Architects, Engineers, and Developers
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ODSC WEST Conference October 30th – November 2nd
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