SystemML

Supports R-like syntax and Python/Java APIs with built-in ML primitives, making it accessible for data scientists familiar with these languages, as highlighted in the README.

Automatically generates hybrid runtime plans combining local and distributed operations on Apache Spark, optimizing execution without manual tuning, per the project's key features.

Includes backends for GPUs and federated learning, providing flexibility for high-performance computing and privacy-preserving scenarios, as noted in the documentation.

Unifies data preparation, model training, debugging, and serving in one system, streamlining the ML workflow from start to finish, based on the overview.