ZenML is an open-source MLOps framework designed to help engineers scale AI and LLM applications beyond prototypes into production. It automates evaluation, monitoring, and deployment, enabling users to manage complex ML pipelines with minimal code changes and track lineage for reproducibility.

How It Works

ZenML provides a Pythonic SDK to define ML pipelines as a series of @step decorated functions. It abstracts away infrastructure complexity, allowing pipelines to run on various cloud providers (AWS, GCP, Azure) and orchestrators (Airflow, Kubeflow, Kubernetes) without code modification. The framework emphasizes artifact tracking, model versioning, and pipeline lineage, facilitating reproducibility and auditability.

Quick Start & Requirements

• Install via pip: pip install "zenml[server]" notebook
• Requires Python 3.9 - 3.12.
• Official docs: https://docs.zenml.io/
• Quickstart guide: Run zenml go

Highlighted Details

• Supports LLMOps, with specific features for continual RAG improvement and reproducible model fine-tuning.
• Integrates with popular tools like LangChain, LlamaIndex, MLflow, and BentoML.
• Offers a VS Code extension for managing stacks and pipeline runs directly within the editor.
• Implements the llms.txt standard for AI-friendly documentation.

Maintenance & Community

• Active community with a Slack group for support and discussions.
• Roadmap is publicly available and influenced by community feedback.
• Contributing guide and "good first issue" labels are provided for new contributors.

Licensing & Compatibility

• Distributed under the Apache License Version 2.0.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

For full functionality, ZenML should be deployed on the cloud to enable collaborative features. A managed ZenML Pro service is available.