About the service
I scale Python workloads on Ray so heavy tasks use all available cores instead of one machine. I own the whole path: distributed training and hyperparameter search, batch and streaming data processing, low-latency model serving, and parallelizing existing code across a cluster. The principle is simple: you move from laptop to cluster without rewriting. Existing functions become remote tasks and actors, logic stays the same.
Usually I get code that works but runs too slow or on too little data. I profile where time actually goes, decide what should be tasks, actors, or datasets. I deploy Ray on your hardware or in the cloud, add autoscaling so idle nodes cost nothing, and make failures recoverable. For training I use Ray Train and Ray Tune, for pipelines Ray Data, for online inference Ray Serve with smart batching and replicas.
You get readable code, a deployment that runs without me, and clear notes on cost and scaling limits. I'll fix a bottleneck rather than add a framework: if multiprocessing is enough, I'll say so.
Usually I get code that works but runs too slow or on too little data. I profile where time actually goes, decide what should be tasks, actors, or datasets. I deploy Ray on your hardware or in the cloud, add autoscaling so idle nodes cost nothing, and make failures recoverable. For training I use Ray Train and Ray Tune, for pipelines Ray Data, for online inference Ray Serve with smart batching and replicas.
You get readable code, a deployment that runs without me, and clear notes on cost and scaling limits. I'll fix a bottleneck rather than add a framework: if multiprocessing is enough, I'll say so.
