Ray
What it is: Distributed computing framework that makes it simple to scale Python applications from laptop to cluster.
What It Does Best
Scale anything Python. Not just ML - any Python code. Add @ray.remote decorator to parallelize functions across cores or machines effortlessly.
Unified ML libraries. Ray Tune (hyperparameter tuning), Ray Train (distributed training), Ray Serve (model serving), Ray Data (data processing) - complete ML platform.
Production-grade distributed computing. Powers Uber, Shopify, OpenAI. Handles fault tolerance, scheduling, and resource management automatically.
Key Features
Ray Core: General-purpose distributed computing
Ray Tune: Scalable hyperparameter optimization
Ray Train: Distributed deep learning training
Ray Serve: Scalable model serving and deployment
Ray Data: Distributed data processing for ML
Pricing
Free: Open source (Apache 2.0 license)
Anyscale: Managed Ray platform with custom pricing
Cloud: Free software, pay for compute infrastructure
When to Use It
✅ Need to scale Python code to clusters
✅ Distributed hyperparameter tuning
✅ Training on multiple machines/GPUs
✅ Building scalable ML applications
✅ Need unified platform for ML workflows
When NOT to Use It
❌ Single machine is sufficient
❌ Simple data parallelism (Horovod simpler)
❌ Batch processing only (Spark better for some cases)
❌ Need mature enterprise support
❌ Team unfamiliar with distributed systems
Common Use Cases
Hyperparameter optimization: Tune models across hundreds of nodes
Distributed training: Train large models on clusters
Reinforcement learning: Parallel RL algorithms (RLlib)
Model serving: Deploy and scale ML inference
Data processing: ETL for machine learning at scale
Ray vs Alternatives
vs Dask: Ray better for ML, Dask better for pure data processing
vs Spark: Ray more flexible, Spark more mature ecosystem
vs Horovod: Ray full platform, Horovod focused on training
Unique Strengths
General-purpose: Not just ML, any Python distributed computing
ML-focused libraries: Complete ecosystem for ML workflows
Production-proven: Powers major companies at scale
Simple API: Easy distributed computing with decorators
Bottom line: Best framework for scaling ML workloads in Python. Unifies training, tuning, and serving in one platform. Essential when you need to move from single machine to clusters. Steeper learning curve but extremely powerful for production ML systems.