Ray Distributed AI Framework is a distributed execution engine providing low-latency task scheduling, distributed actors, and object store for efficient machine learning and AI workloads, enabling fine-grained parallelism with minimal overhead — optimized for dynamic, heterogeneous AI computations. Ray unifies batch, streaming, and serving. Tasks and Parallelism @ray.remote decorator designates functions as distributed tasks. task.remote() submits asynchronously, returning ObjectRef (future). ray.get() blocks retrieving result. Fine-grained task submission enables dynamic parallelism without DAG pre-specification. Actors and Stateful Computation @ray.remote classes define actors—processes maintaining state. Actors handle multiple method calls sequentially, enabling stateful service. Useful for parameter servers, replay buffers, rollout workers. Distributed Object Store Ray's object store enables efficient data sharing: local store on each node, distributed with replication. Objects auto-spilled to external storage (S3, HDFS) if memory insufficient. Zero-copy sharing: tasks on same node access object in local store without serialization. Scheduling and Locality scheduler assigns tasks to nodes considering data locality and resource requirements. CPU/GPU resource specification ensures proper placement. Minimizes data movement. Fault Tolerance lineage-based recovery: Ray tracks task dependencies, re-executes failed tasks recomputing lost data. Effective for deterministic tasks. Ray Tune hyperparameter optimization: automatic distributed hyperparameter search with early stopping, population-based training. Ray RLlib reinforcement learning library: distributed training algorithms (A3C, PPO, QMIX). Actors organize rollout workers, training workers, parameter servers. Ray Serve serving predictions from trained models. Ray Data distributed data processing with lazy evaluation, similar to Spark but Ray-optimized. Named Actor Handles actors can be named and retrieved globally, enabling loosely-coupled microservice architectures. Dynamic Task Graphs unlike static DAG frameworks (Spark, Dask), Ray supports dynamic task creation—task outcomes determine future tasks. Essential for tree search, early stopping, RL. Heterogeneous Resources specify CPU, GPU, memory, custom resources. Scheduler respects constraints. Applications include hyperparameter optimization, reinforcement learning training, distributed ML inference, batch RL, parameter sweeps. Ray's fine-grained scheduling, distributed object store, and dynamic task graphs make it ideal for heterogeneous, resource-intensive AI workloads compared to traditional batch frameworks.