ZenML: The Open-Source AI Platform That Takes You From Pipelines to Agents
ZenML is an open-source AI platform for production ML: reproducible pipelines, cloud-native stacks, and native LLMOps and agent support — all as code.
Machine learning in production is hard. You train a model on your laptop, and then everything changes: data sources, infrastructure, monitoring, retraining schedules. ZenML is the open-source AI platform that tackles this head-on — one platform that takes you from pipelines to agents, with MLOps best practices built in.
🚀 Want to deploy ZenML yourself?
Docker configs, system requirements, and installation guides — all on one page.
View ZenML Tool Page →What Makes ZenML Different?
ZenML treats your ML workflows as code. Instead of stitching together notebooks, shell scripts, and one-off cron jobs, you define reproducible pipelines that run identically on your laptop or in the cloud. Your training, evaluation, and deployment steps become versioned, cacheable, and auditable.
Core Capabilities
| Capability | Why It Matters |
|---|---|
| Pipeline orchestration | Declare steps as Python code, run them on any backend |
| Stack abstraction | Swap orchestrators, artifact stores, and container registries without rewriting code |
| Model registry & tracking | Version models, datasets, and experiments automatically |
| LLMOps & agents | Native support for agent workflows, RAG, and LLM evaluation |
| Cloud-native | Runs on Kubernetes, AWS, GCP, and Azure out of the box |
📦 Deployment at a glance: ZenML is Apache-2.0 licensed with an official Docker image (zenmldocker/zenml, 60K+ pulls). The server ships in a single container, and the CLI connects to it over gRPC/REST.
From Pipelines to Agents
The most exciting shift in ZenML is its evolution beyond classical MLOps. With LLMOps support, you can build RAG pipelines, evaluate LLM outputs, and orchestrate AI agent workflows in the same framework you already use for traditional ML. That means one control plane for your whole AI lifecycle — no more duct-taping five different tools together.
🚀 Want to deploy ZenML yourself?
Docker configs, system requirements, and installation guides — all on one page.
View ZenML Tool Page →Should You Self-Host It?
If you are building ML or AI systems that must be reproducible, auditable, and portable, ZenML is an excellent foundation. It is MIT-community friendly, has a large ecosystem of integrations, and its server can be deployed on a single machine or scaled to Kubernetes. Check the tool page for system requirements and a ready-made docker-compose setup.