MLRun logo


Pipeline orchestration
Artifact tracking
Model registry
Model serving
Model monitoring

MLRun is an open-sourced MLOps framework that provides end-to-end machine learning management from R&D to production deployment.

Use it when

  • You want to set up ML tasks (monitoring, data movement, scaling, versioning, CI/CD integration, and serving) as serverless functions.
  • You want to visualize pipelines as DAGs (Direct Acyclic Graphs).
  • You want ETL pipelines to extract batch data from multiple sources and run data transformations.
  • You want an integrated feature store.
  • You want easy built-in functions to test production pipelines locally before deployment.
  • You want a marketplace for ML functions to improve your pipelines with minimal engineering efforts.

Watch out

  • The self-hosted version requires a Kubernetes cluster. It is possible to run MLRun locally with Docker Desktop or minikube.
  • Many functions and services are only available out of the box in managed service offered by Iguazio.

Example stacks

Airflow + MLflow stack


kubectl create namespace mlrun
helm repo add v3io-stable
helm repo update
kubectl --namespace mlrun create secret docker-registry registry-credentials     --docker-server <your-registry-server>     --docker-username <your-username>     --docker-password <your-password>     --docker-email <your-email>
helm --namespace mlrun     install mlrun-kit     --wait     --timeout 960s     --set global.registry.url=<registry-url>     --set global.registry.secretName=registry-credentials     v3io-stable/mlrun-kit