Netflix/metaflow
Build, Manage and Deploy AI/ML Systems observed · 2026-08-28
Health v2 · maintenance only
95/100
- Activity 99
- Release rhythm 86
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 4
- age_days: 2542
- days_rel: 16
- days_push: 8
- n_releases_24m: 126
Adoption not part of the score
10245 stars · 1336 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Metaflow is a human-centric Python framework from Netflix for building, managing, and deploying real-life AI/ML and data science systems. It unifies local prototyping, experiment tracking, scalable cloud compute (CPUs/GPUs on Kubernetes or AWS Batch), dependency management, and one-click production deployment in a single API.
Use cases
- orchestrate machine learning pipelines in plain python
- scale model training to gpus in the cloud
- track experiments and version artifacts automatically
- deploy ml workflows to production with one command
- run thousands of parallel data processing tasks
- prototype in notebooks then move to production without rewriting
- build and deploy llm training or agent workflows
When to choose
- your team needs to take ML prototypes from laptop to production reliably
- you want experiment tracking, versioning, and orchestration in one pythonic API
- you need to scale compute across cloud providers or Kubernetes without changing code
- you want a battle-tested framework proven at Netflix-scale
When to avoid
- you only need simple cron-style scheduling without ML workflow features
- your project is a lightweight ETL script with no scaling or tracking needs
- you require a visual drag-and-drop pipeline builder rather than code-first workflows
- you cannot deploy any infrastructure, since cloud features require a Metaflow stack
Facets
framework · maturity stable
machine-learning workflow-automation scheduling etl llm-training data-science developer-tools machine-learning data-science large-language-models cloud-computing python cloud mlops ml-platform experiment-tracking workflow-orchestration llmops distributed-computing pipelines model-management ai-agents devops kubernetes macos linux docker
8 sources
- readme: https://github.com/Netflix/metaflow · fetched 2026-08-28 · 389ef18c0171
- homepage: https://metaflow.org · fetched 2026-08-29 · a39f2b334088
- site_page: https://docs.metaflow.org/getting-started/tutorials · fetched 2026-08-29 · 4c0568745fce
- site_page: https://docs.metaflow.org/ · fetched 2026-08-29 · a3a928226a18
- site_page: https://docs.metaflow.org/getting-started/install · fetched 2026-08-29 · 3c7cb6c595da
- site_page: https://docs.metaflow.org/getting-started/devstack · fetched 2026-08-29 · 5e658873c7a1
- site_page: https://docs.metaflow.org/scaling/remote-tasks/introduction · fetched 2026-08-29 · 585e14a2d3dd
- registry_pypi: https://pypi.org/pypi/metaflow/json · fetched 2026-08-29 · cafcff52840a
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Netflix/metaflow | main | 95 |
For agents
markdown · JSON · MCP: product_card(name="Netflix/metaflow")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem