Accelerating Model Deployment Velocity

Posted Apr 12, 2022 | Views 969

# Model serving

# Organization and Processes

# Production Use Case

# Systems and Architecture

SPEAKER

Emmanuel Ameisen

Staff Machine Learning Engineer @ Stripe

Emmanuel Ameisen has worked for years as a Data Scientist and ML Engineer. He is currently an ML Engineer at Stripe, where he worked on helping improve model iteration velocity. Previously, he led Insight Data Science's AI program where he oversaw more than a hundred machine learning projects. Before that, he implemented and deployed predictive analytics and machine learning solutions for Local Motion and Zipcar. Emmanuel holds graduate degrees in artificial intelligence, computer engineering, and management from three of FranceÕs top schools.

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SUMMARY

All ML teams need to be able to translate offline gains to online performance. Deploying ML models to production is hard. Making sure that those models stay fresh and performant can be even harder. In this talk, we will cover the value of regularly redeploying models, and the failure modes of not doing so. We will discuss approaches to make ML deployment easier, faster and safer which allowed our team to spend more time improving models, and less time shipping them.

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