Python provides convenience and flexibility for scalable ML/AI

Written by Dean Wampler, Anyscale

Anyscale develops Ray (ray.io), an open-source suite of tools that make it much easier to scale Python applications from laptops to clusters. Ray was created at U.C. Berkeley by ML/AI researchers who needed flexible tools for scaling the heterogenous, large-scale workloads common in reinforcement learning, hyperparameter tuning, etc., especially when large neural networks are used.

Of course, Python is the most popular language for ML/AI, because of its convenience. Python's flexibility also allows Ray to instrument Python code to make ML/AI scalability possible without requiring distributed systems expertise and lots of invasive code changes. Hence, ML/AI users get the benefits of cluster-wide scalability with minimal effort.