What I liked about Anyscale is how it simplifies running and scaling AI/ML workloads. It provides a flexible environment for developing and deploying Ray-based applications without having to manage as much of the underlying infrastructure. The ability to scale workloads when needed while keeping development and deployment in one place had been particularly useful for our team.

The best thing I like about this platform is its ability to make it easier to develop and scale AI and machine learning workloads just using Ray technology. Also, I like that the same code can move from different interactive workspaces to schedule jobs and protection services, along with minimal changes. Its automatic scaling, workload monitoring and dependency management reduce a lot of infrastructure work. Also, the console provides useful visibility into logs, metrics and other resource usage. Overall, it allowed our team to focus more on the model and applications than cluster management.

Anyscale has made scaling training workloads for our customer support assistant much more manageable, distributing compute-intensive tasks across a cluster without needing to manually manage the underlying infrastructure. Being built on Ray meant the underlying distributed computing framework is battle-tested, giving confidence that scaling wouldn't introduce unexpected instability. The interface for managing clusters and jobs is straightforward, letting the team submit and monitor training runs without deep distributed systems expertise. Integration with our existing Python-based training code was smooth, requiring minimal changes to take advantage of distributed execution.