Ray for ML — what does it provide?

medium

Answer

$Distributed Python framework.$
$(1) Ray Core: distributed tasks + actors.$
$(2) Ray Train: distributed training (PyTorch / TF wrappers).$
$(3) Ray Tune: hyperparameter search.$
$(4) Ray Data: distributed data loading.$
$(5) Ray Serve: model serving.$
$(6) RLlib: distributed RL.$
$One framework covering training \to tuning \to serving.$
$Common on Kubernetes via KubeRay.$
$Alternatives: Dask (arrays / DataFrames), Spark (batch + SQL).$

Check yourself — multiple choice

Random
Distributed Python: Core (tasks/actors) + Train + Tune + Data + Serve + RLlib; one framework across training→tune→serve; K8s via KubeRay; alternatives Dask/Spark
Just serving
Not real

Ray: distributed Python; Train + Tune + Data + Serve + RLlib.

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