ob-metaflow
Metaflow: More AI and ML, Less Engineering
Decision gist · record as of 2026-08-14
Yes. ob-metaflow is actively maintained, has low install friction, carries no known vulnerabilities, and uses a permissive license. It is well-suited for teams building ML systems that need to move from prototyping to production without significant refactoring. Start with local prototyping and scale incrementally as your infrastructure needs grow.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure-Python wheel distribution.
- Active maintenance with a release 3 days old.
- Runtime dependencies on requests, boto3, pylint, and kubernetes are standard infrastructure libraries.
License · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 is permissive, allowing commercial use, modification, and distribution with minimal restrictions—suitable for most organizational contexts.
last release 2026-08-11 (3 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 113,330 downloads/mo, #12,344 on PyPI
Alternatives
Verify before relying
pip install ob-metaflow
from metaflow import FlowSpec, step
class MyFlow(FlowSpec):
@step
def start(self):
self.next(self.end)
@step
def end(self):
pass
if __name__ == '__main__':
MyFlow()- Minimum Python version requirement (requires_python is unspecified in the fact sheet)
- Whether kubernetes dependency is required or optional for local prototyping
- GPU support specifics and any system-level dependencies beyond the listed runtime packages
What it is and what it does
ob-metaflow is a framework for building AI and ML systems that spans the entire lifecycle from local prototyping in notebooks to reliable production deployments. It unifies code, data, and compute management, providing a Pythonic API for experiment tracking, versioning, and visualization alongside support for scaling workloads horizontally and vertically across cloud infrastructure using CPUs and GPUs.
The framework handles dependency management, data access, and orchestration, with built-in support for both embarrassingly parallel and gang-scheduled compute jobs. It integrates with cloud providers via boto3 and kubernetes, and supports deployment to production orchestrators with reactive event triggering. Originally developed at Netflix and now supported by Outerbounds, it is designed for teams ranging from individual researchers to large organizations running thousands of concurrent workflows.
Use it for
- Prototype ML models locally in a notebook, then scale the same code to distributed cloud compute without rewriting
- Track and version experiments across multiple runs, comparing results and managing artifacts automatically
- Deploy a trained model as a production workflow that triggers on events and scales based on demand
- Run embarrassingly parallel data processing jobs across many machines with built-in failure recovery
- Manage dependencies and orchestrate multi-stage pipelines with clear data lineage between steps
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
ob-metaflow is actively maintained, has low install friction, carries no known vulnerabilities, and uses a permissive license. It is well-suited for teams building ML systems that need to move from prototyping to production without significant refactoring. Start with local prototyping and scale incrementally as your infrastructure needs grow.
Install
ob-metaflow on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Active maintenance with a release 3 days old. Runtime dependencies on requests, boto3, pylint, and kubernetes are standard infrastructure libraries.
License in practice
Apache License 2.0 is permissive, allowing commercial use, modification, and distribution with minimal restrictions—suitable for most organizational contexts.
Quickstart
pip install ob-metaflow
from metaflow import FlowSpec, step
class MyFlow(FlowSpec):
@step
def start(self):
self.next(self.end)
@step
def end(self):
pass
if __name__ == '__main__':
MyFlow()Verify before relying
- Minimum Python version requirement (requires_python is unspecified in the fact sheet)
- Whether kubernetes dependency is required or optional for local prototyping
- GPU support specifics and any system-level dependencies beyond the listed runtime packages
Package facts
| License | Apache License 2.0 permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesrequestsboto3pylintkubernetes |
| Maintenance | Actively maintained 3 days since the last release |
| First released | |
| Downloads | 113,330 / month, #12,344 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: ob_metaflow-2.19.37.1-py2.py3-none-any.whl
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See also metaflow · ob-metaflow-extensions · ob-project-utils · metaflow-stubs · outerbounds · metaflow-prebuilt · azureml-pipeline · zenml · mlflow · kfp-pipeline-spec