Operationalizing ML at Scale
summary
Covers how AI/ML platform design changes when moving from web-scale environments (Facebook, Google) to autonomous vehicle systems at Cruise, focusing on the different notions of scale and impact in production ML. Discusses platform engineering and product considerations for operationalizing ML across those domains.
AI-written summary · about
related talks
21mML Infrastructure for Autonomous Vehicles
37mOptimizing AI and ML with Platform Engineering
31mProductionizing ML with ML Ops and Cloud AI
30mEssential Ingredients in Scaling Organizations for ML
54mML Beyond Metrics: Challenges of Using ML Systems in the Real World
47mMLOps tools to automate ML deployment
50mMLOps for a hybrid AI platform compliant with European law
13mMLOps at Scale