Kubeflow

Modern workflow orchestration for data pipelines

Prefect is an open-source workflow orchestration framework with 18K+ GitHub stars providing a Python-native approach to building, scheduling, and monitoring data pipelines. Turns any Python function into a schedulable, observable workflow with decorators. Features automatic retries, caching, concurrency controls, event-driven triggers, and a modern dashboard. Easier to adopt than Airflow with less boilerplate. Prefect Cloud provides managed orchestration with team collaboration features.

ML model serving and deployment framework

BentoML is an open-source framework with 7K+ GitHub stars for packaging, deploying, and serving ML models as production-ready APIs. Bundles models, preprocessing, and serving logic into portable Bento archives with auto-generated REST/gRPC endpoints. Features adaptive batching for throughput optimization, GPU scheduling, multi-model inference pipelines, and containerization. Supports all major ML frameworks including PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.

Evaluation framework for RAG pipelines

RAGAS is an Apache-2.0 open-source evaluation framework with 14K+ GitHub stars that provides standardized metrics for assessing RAG pipeline quality. It measures faithfulness, answer relevancy, context precision, and context recall to identify whether retrieval, generation, or both are failing. It is framework-agnostic, supports LLM-as-judge evaluation, and its README discloses minimal anonymized Open Analytics with a RAGAS_DO_NOT_TRACK opt-out.