ZenML
Open source MLOps framework (Apache 2.0) that turns your ML code into versioned, reproducible pipelines instead of throwaway scripts. You write your steps in Python, ZenML tracks every artifact, model and run in one metadata store, and the same pipeline runs locally for debugging then on Kubernetes or in the cloud without changing your logic. It covers classical ML (scikit-learn, PyTorch) as well as LLM and agent pipelines (LangGraph, LlamaIndex), with 60+ integrations to wire in your existing tools.
Strengths
- Reproducible, versioned pipelines: every run is replayable identically, artifacts and metadata tracked
- The same code runs locally then on Kubernetes or cloud without rewriting the logic
- Covers classical ML AND LLM/agent pipelines, 60+ integrations to wire in what you already use
Limitations
- A real learning curve: disproportionate for a small project or a simple idea test
- The full benefit (Kubernetes, managed control plane) assumes infrastructure not everyone has
Best for
- Turning a paper replication into a reproducible, credible portfolio project
- Proving concrete MLOps skill (versioning, lineage, replayability)
- Structuring an ML or LLM pipeline that must move from your machine to prod without a rewrite