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Join us as we explore what it takes to

Bring machine learning innovations to life

Virtual Event | March 2, 2022

Companies today are utilizing machine learning to deliver outsized business outcomes. However, developing ML is incredibly challenging. It’s a slow, iterative process; one that is complex, with many moving parts, diverse stakeholders, and potential pitfalls. The field is nascent, with tools and techniques changing quickly. Sharing insights from successful machine learning projects across industries advances ML and accelerates its potential to solve challenging problems.

Join us at CONVERGENCE 2022 to learn from leaders driving innovation with machine learning, who have faced development challenges and successfully delivered business value with machine learning. In this virtual event, you will discover emerging tools, approaches, and workflows that can help you effectively manage an ML project from start to finish. Choose from business and technical tracks with presentations from experts in data science and machine learning, who will share their best practices and insights on developing and implementing enterprise ML strategies.


Resham Sarkar

Sr Manager – Data Science at Slice

Oren Etzioni

CEO at Allen Institute for Artificial Intelligence (AI2)

Shivika Bisen

Lead Data Scientist at PAXAFE

Uri Goren

Head of Recommendation at Argmax

Emily Curtin

Senior Machine Learning Engineer at Mailchimp

Eduardo Bonet

Staff Full Stack Engineer – MLOps  at GitLab

Saira Kazmi

Senior Director – Enterprise Data Engineering Strategy and AI at CVS Health

Abubakar Abid

Machine Learning Team Lead at Hugging Face

Sanjay Yermalkar

Sr. Director, Data Science Engineering at Anthem

Kevin Stumpf

Co-Founder and CTO at Tecton

Peter Gao

Co-Founder and CEO at Aquarium Learning

Gideon Mendels

Co-Founder and CEO at Comet

Alexander Izydorczyk

Head of Data Science at Coatue Management


Business Track

Using ML to drive innovation and achieve business goals

The importance of domain expertise in ML

Data-centric AI

Strategies for overcoming data management challenges

Requirements for building a successful ML platform

How to build a future-proof ML tech stack

Technical Track

Using CI/CD pipelines for model development and deployment

Testing ML models for production

Accelerating ML iteration for better model outcomes

Distributed training strategies and techniques

Using visualization to improve data understanding

Participating companies

And many more!


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March 2, 2022