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Experiment tracking, model registry & production monitoring for ML teams
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Human‑centric framework for building, scaling, and deploying AI systems
Metaflow lets data scientists and engineers prototype in notebooks, track experiments, and seamlessly scale to cloud CPUs/GPUs, then deploy reproducible ML workflows with one‑click production orchestration.
Metaflow is a Pythonic framework aimed at data scientists, researchers, and ML engineers who start their work in notebooks. It offers built‑in experiment tracking, versioning, and visualization, allowing teams to iterate quickly while keeping code, data, and model artifacts unified throughout the lifecycle.
When projects outgrow a local environment, Metaflow scales horizontally and vertically across AWS, Azure, GCP, and on‑prem Kubernetes, supporting both CPU and GPU workloads—from embarrassingly parallel sweeps to gang‑scheduled jobs. With a single command, flows are packaged with their dependencies and deployed to production‑grade orchestrators, enabling reactive orchestration and reliable, maintainable AI systems.
The framework is backed by Netflix and Outerbounds, trusted by thousands of users at companies like Amazon, DoorDash, and Goldman Sachs, and is supported by an active community on Slack.
When teams consider Metaflow, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Rapid notebook prototyping
Iterate quickly on new algorithms with built‑in versioning and visualizations, then promote the notebook to a reusable flow.
Large‑scale hyperparameter sweeps
Execute thousands of parallel runs on an AWS GPU fleet, automatically aggregating results for analysis.
Production deployment to Kubernetes
One‑click rollout of a fraud detection model to a Kubernetes orchestrator with reactive monitoring and auto‑retries.
Foundation model fine‑tuning lifecycle
Track data, code, and fine‑tuned model artifacts from experiment through serving, ensuring reproducibility and auditability.
Yes, Metaflow is released under the Apache‑2.0 license.
Metaflow works with AWS, Azure, GCP, and on‑prem Kubernetes clusters.
No, pipelines are defined directly in Python using Metaflow decorators.
It captures environment specifications and can use Conda or Docker images to ensure reproducible runs.
Yes, flows can be triggered from CI pipelines and export artifacts for downstream consumption.
Project at a glance
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