Overview
ZenML is a unified MLOps framework that extends classical MLOps principles to AI agents. It provides pipelines, versioning, evaluation and deployment primitives that let teams collaborate across model and agent development without maintaining separate toolchains.
Key Features
- Pipeline and step abstractions for reproducible training, evaluation and deployment.
- Integrations with orchestration backends (local, Kubernetes), experiment trackers (MLflow, W&B) and agent/LLM tooling (LangGraph, LiteLLM).
- Built-in support for agent evaluation, prompt versioning and data-driven architecture comparison.
Use Cases
- Automate model lifecycle in CI/CD: training, testing, deployment and monitoring.
- Evaluate multiple agent architectures on real data and promote the best candidate to production.
- Connect pipelines to monitoring and tracing systems for model health and performance analysis.
Technical Highlights
- Python-first SDK and CLI with rich examples and tutorials.
- Deployable as server/client (ZenML Server) or via Helm/Docker for production environments.
- Active community, comprehensive docs and enterprise options (ZenML Pro).