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Basic Information

ZenML is an open source MLOps framework that unifies classical machine learning workflows and modern AI agent development under a single platform. It helps teams version, test, deploy, and monitor everything from scikit-learn models to complex LLM-based agents by applying established MLOps principles to agent development. The project provides pipeline and step abstractions to wrap existing code, treats prompts and agent configurations as versioned artifacts, and connects development, evaluation, and production deployment with full lineage. ZenML targets ML engineers who want to avoid maintaining separate stacks for models and agents by offering reproducible pipelines, integrated observability, and documented examples for agent comparison, RAG, fine-tuning, and end-to-end inference. The repo includes client-server components, a web dashboard, and integrations to common tooling so teams can run locally or deploy to production infrastructure.

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Features
ZenML offers a declarative pipeline and step API to compose reproducible workflows that cover training, evaluation, and deployment. It versions artifacts including prompts and model code, supports materializers and custom visualizations, and provides integrations with LLM tools and observability platforms such as LiteLLM, LangGraph, and Langfuse. The project includes an MCP server to query and control pipelines via natural language, a VS Code extension for in-editor pipeline management, and first-class support for experiment trackers like MLflow and Weights & Biases. Deployment options include local server mode and self-hosted production deployments with Docker, Helm/Kubernetes, object storage, and supported databases. Examples demonstrate agent architecture comparisons, RAG pipelines, agentic workflows, fine-tuning, and batch inference to help users adapt patterns to their stacks.
Use Cases
ZenML helps teams bring discipline and reproducibility to both traditional ML and modern agent development by extending familiar MLOps practices across the entire AI stack. It reduces operational overhead and fragmentation by letting engineers reuse the same pipeline, testing, and CI/CD patterns for models and agents, and by versioning prompts and configurations alongside code. Users gain data-driven decision making via reproducible evaluation pipelines and visual reports, improved observability through integrations with tracing and monitoring tools, and easier operations with the MCP conversational interface for querying runs and triggering pipelines. The framework supports local development and scalable production deployments, enabling teams to iterate faster, maintain lineage from data to deployments, and avoid running parallel stacks for models and agents.

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