What impresses me most is how it handles the heavy lifting for Ray. I can develop my AI application code right on my laptop and then deploy it to a large cluster without having to rewrite anything or wrestle with complex infrastructure setups. This effectively bridges the gap between code that only "works on my machine" and a real production environment, which is particularly useful when scaling LLM workloads and managing distributed training. In the end, it saves me a considerable amount of time on DevOps tasks.

Anyscale makes it easy to scale AI/ML workloads without worrying about infrastructure complexity

Anyscale simplifies the process of moving AI and ML workloads from development to production. Since it is built on Ray, it enables scalability without requiring major code changes.