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A Masterclass in Delivering High-Velocity ML at Scale

With over 30 sessions, TWIMLcon On Demand offers a masterclass in how to design and build teams and platforms for delivering machine learning in a high-velocity, repeatable, consistent and predictable manner—at scale:


A masterclass in delivering enterprise ML & AI.
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Build your ML Operationalization Knowledge Through Case Studies, How-Tos, Perspective Talks, Panels and Workshops


How Spotify Does ML at Scale

Aman Khan Spotify

Aman Khan

Product Manager

John Baer Spotify

Josh Baer

Machine Learning Platform Product Leader

When Good Models Go Bad: The Damage Caused by Wayward Models and How to Prevent it

Todd Underwood

Josh Baer

Engineering Director

Machine Learning is Going Real-Time: Are You Ready?

Chip Huyen

Chip Huyen

Machine Learning Engineer at Snorkel AI, Adjunct Lecturer at Stanford

Cost Transparency and Model ROI at Intuit

Ian Sebanja

Ian Sebanja

Machine Learning Platform

Srivathsan Canchi

Srivathsan Canchi

Head of Engineering, ML Platform Team

Driving Platform Adoption Success at Spotify

James Kirk

James Kirk

Staff Machine Learning Engineer, Listening Experiences

Lex Beattie

Lex Beattie

Machine Learning Engagement Lead

Maisha Lopa

Maisha Lopa

Senior Product Manager, Machine Learning Platform

Sam N

Samuel Ngahane

Staff ML Software Engineer

Maya Hristakeva

Maya Hristakeva

Machine Learning Engineering Manager

Build Smarter, Innovate Faster and Avoid Costly Mistakes

TWIMLcon sessions—presented by data scientists, engineers and leaders from teams at the forefront of operationalizing machine learning—can help you and your team save time and money by learning: