{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/16"},{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/2"}],"enrichment":{"capability":"ZenML is an MLOps orchestration platform that lets you write pipelines and deploy them across any infrastructure backend, automatically handling containerization, run tracking, and integration with existing ML tools.","skillfed_tags":["mlops","orchestration","agentic-workflows"],"use_cases":["Orchestrate end-to-end ML pipelines with feature engineering, model training, and batch inference across distributed infrastructure.","Deploy and monitor LLM applications and RAG systems with built-in evaluation loops and production observability.","Build and manage agentic workflows that integrate with existing Python tools and frameworks with automatic run tracking.","Unify classical ML model deployment and AI agent management in a single framework without rewriting existing code.","Iterate rapidly on experiments in development with automatic artifact versioning, then promote to production without changes."],"what_it_does":"ZenML is an MLOps orchestration framework designed for teams running traditional ML, LLM workflows, or agentic systems in production. It abstracts infrastructure complexity by letting you write pipelines in Python, then automatically containerizes, tracks, and deploys them across any backend\u2014local, Kubernetes, cloud providers, or custom stacks. At its core, you wrap your existing code in @step and @pipeline decorators; ZenML then handles containerization, artifact tracking, metrics logging, and integration with tools like MLflow, Weights & Biases, and cloud platforms.\n\nThe platform is built on a client-server architecture with an integrated web dashboard. You can start locally with `pip install \"zenml[local]\"` for development, then deploy the server separately and connect via `zenml login` for production. It supports Python 3.10 through 3.14 and depends on well-maintained libraries (pydantic, docker, opentelemetry, click, rich) for configuration, containerization, observability, and CLI interaction. The project is actively maintained, with recent releases and thousands of companies using it.","worth_installing":"Yes. ZenML is actively maintained, has low install friction, uses a permissive Apache-2.0 license, and has no known security vulnerabilities. It solves a real problem\u2014orchestrating ML and AI workflows across infrastructure\u2014without requiring you to rewrite existing code. Suitable for teams moving from ad-hoc scripts to production MLOps, though the 17 runtime dependencies and learning curve around stacks warrant evaluation in your specific environment."},"id":"zenml","links":{"html":"https://skillfed.io/packages/zenml","md":"https://skillfed.io/packages/zenml.md","pypi":"https://pypi.org/project/zenml/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"zenml","python_support":"supports_current","summary":"ZenML: MLOps for Reliable AI: from Classical AI to Agents."},"popularity":{"monthly_downloads":198588,"position":9725,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.96.3"}