A GitOps Approach to Machine Learning in Production

Explore a conference talk from MLOps World: Machine Learning in Production that delves into the application of GitOps principles to machine learning in production. Learn how Interos implements GitOps for most of their MLOps work, storing ML configurations as code. Discover the numerous benefits of GitOps, including traceability, stability, reliability, consistency, enhanced productivity, and providing a single source of truth. Gain insights into how GitOps is applied to deployment configurations, onboarding processes, monitoring configurations, and all stages of the model lifecycle. Understand how the portable and declarative nature of GitOps has led to increased traceability and development capacity for small teams. Presented by Amy Bachir, Senior MLOps Engineer, and Stephan Brown, MLOps Engineer, both from Interos, this 34-minute talk offers valuable perspectives from experienced practitioners in the field of MLOps.

A GitOps Approach to Machine Learning