Comet
Experiment tracking, model registry & production monitoring for ML teams
Metaflow
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.
Overview
Accelerate AI/ML Development
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.
Scale and Deploy with Confidence
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.
Highlights
Pythonic API for notebook‑first prototyping with built‑in experiment tracking
Seamless horizontal and vertical scaling on AWS, Azure, GCP, and Kubernetes with CPU/GPU support
One‑click deployment to production‑grade orchestrators and reactive workflow management
Unified handling of code, data, and model artifacts across the entire ML lifecycle
Pros
- Accelerates research‑to‑production cycles
- Handles petabyte‑scale data and model artifacts
- Supports both embarrassingly parallel and gang‑scheduled workloads
- Strong community and enterprise backing (Netflix, Outerbounds)
Considerations
- Primarily Python‑centric, limiting non‑Python ecosystems
- Advanced scaling features require cloud infrastructure configuration
- Learning curve for reactive orchestration concepts
- May be overkill for very small, single‑script experiments
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Fit guide
Great for
- Data science teams needing notebook‑driven experimentation and reproducible pipelines
- ML engineering groups scaling workloads across cloud CPUs and GPUs
- Enterprises managing large numbers of models and artifacts
- Organizations seeking a unified framework for experiment tracking, versioning, and production deployment
Not ideal when
- Projects confined to a single local script without scaling needs
- Teams heavily invested in non‑Python ML stacks
- Environments lacking cloud access or orchestration platforms
- Very small teams preferring lightweight script‑only tools
How teams use it
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.
Tech snapshot
Python93%
R3%
Svelte2%
HTML1%
Starlark1%
TypeScript1%
Frequently asked questions
Is Metaflow free to use?
Yes, Metaflow is released under the Apache‑2.0 license.
Which cloud providers are supported?
Metaflow works with AWS, Azure, GCP, and on‑prem Kubernetes clusters.
Do I need to write YAML for pipelines?
No, pipelines are defined directly in Python using Metaflow decorators.
How does Metaflow handle dependencies?
It captures environment specifications and can use Conda or Docker images to ensure reproducible runs.
Can Metaflow integrate with existing CI/CD tools?
Yes, flows can be triggered from CI pipelines and export artifacts for downstream consumption.
Project at a glance
Active- Stars
- 9,724
- Watchers
- 9,724
- Forks
- 941
LicenseApache-2.0
Repo age6 years old
Last commit17 hours ago
Primary languagePython
Last synced 4 hours ago