Ray readiness review

Apply the readiness checklist to a production candidate.

Ray production readiness A production Ray application is more than a working notebook. It needs resource intent, dependency control, observability, failure handling, and operational ownership. Readiness dimensions Area Question Resources Are CPU, GPU, and memory needs explicit? Data Can workers read inputs directly? Failure What retries or checkpoints exist? Observability Which metrics indicate progress and saturation? Release Can the team roll back code and model versions? Resource annotations @ray.remote(num_cpus=4, num_gpus=1) def gpu_transform(batch): return run_model(batch) Operating principle Make the cluster behavior legible. If a task needs a GPU, say so. If a pipeline depends on object storage throughput, measure it. If a Serve deployment owns user traffic, define health and rollback expectations.

Area	Question
Resources	Are CPU, GPU, and memory needs explicit?
Data	Can workers read inputs directly?
Failure	What retries or checkpoints exist?
Observability	Which metrics indicate progress and saturation?
Release	Can the team roll back code and model versions?

Readiness review