ML Model Deployment
Robert discusses bottlenecks in scaling ML projects and the challenges of serving machine learning models in production. He highlights the limitations of existing frameworks and introduces Ray serve as a solution for combining ML models with application logic efficiently.In this clip
From this podcast
Gradient Dissent - A Machine Learning Podcast
Robert Nishihara — The State of Distributed Computing in ML
Related Questions