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.
Speakers
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
SAMPLE TOPICS TO BE COVERED
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!
Register
Brought to you by
Where
Online
When
March 2, 2022
Contact
cometml@formulated.by