mlrun/mlrun

Health v2 · maintenance only

Adoption not part of the score

1692 stars · 317 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MLRun is an open-source MLOps and AI orchestration framework for building, training, deploying, and monitoring machine learning and generative AI applications across their lifecycle. It integrates with development and CI/CD environments, automating data pipelines, model serving, and experiment tracking on Kubernetes and multi-cloud infrastructure.

Use cases

• orchestrate end-to-end ML training and deployment pipelines
• track experiments, data lineage, and model versions
• deploy real-time model serving endpoints with autoscaling
• automate LLM fine-tuning and serving workflows
• run distributed data processing and feature engineering jobs on Kubernetes
• add MLOps and monitoring to existing ML code with minimal changes
• manage batch and streaming inference pipelines in production

When to choose

• you need an end-to-end MLOps platform covering training through serving and monitoring
• your team runs ML workloads on Kubernetes or multi-cloud/hybrid infrastructure
• you want experiment tracking, lineage, and model serving from one framework
• you need to productionize ML or gen AI pipelines with CI/CD automation

When to avoid

• you only need lightweight experiment tracking without orchestration
• you run simple single-machine ML without Kubernetes or cloud infrastructure
• you prefer composing individual tools like MLflow, Kubeflow, and Seldon yourself
• your project cannot adopt a Python-centric SDK and platform services

Facets

framework · maturity active

machine-learning workflow-automation monitoring deployment etl llm-training llm-inference scheduling machine-learning data-science large-language-models python cloud self-hosted mlops experiment-tracking model-serving orchestration pipelines genai ci-cd model-monitoring feature-store data-engineering kubernetes automation docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="mlrun/mlrun")