pystac-ext-mlm
Machine Learning Model extension for PySTAC
Decision gist · record as of 2026-08-14
Yes. Active maintenance (released 4 days ago), Production/Stable status, permissive Apache-2.0 license, no known vulnerabilities, and low install friction make this a safe choice. Install it if you are already using PySTAC and need to catalog machine learning models with standardized metadata. The tight coupling to pystac-core and related extensions means it integrates seamlessly into existing PySTAC workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10; pystac-core and related extensions must be installed as runtime dependencies.
- Low friction install with active maintenance.
- Released 4 days ago, last commit 2026-08-10, and marked Production/Stable.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions—you can use, modify, and distribute this code freely provided you include the license notice.
last release 2026-08-10 (4 days) · last repo commit 2026-08-10 · 454 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,981,844 downloads/mo, #2,417 on PyPI
Alternatives
Verify before relying
pip install pystac-ext-mlm
from pystac_ext_mlm import MLM
# Use MLM extension to add ML model metadata to a STAC item
mlm = MLM.from_item(item)- Whether the extension supports all major ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.) or a specific subset.
- Whether model versioning and lineage tracking are supported beyond the core architecture/framework fields.
- Performance characteristics when working with large STAC catalogs containing many ML model items.
What it is and what it does
pystac-ext-mlm is a PySTAC extension package that implements the Machine Learning Model extension specification for STAC (SpatioTemporal Asset Catalog). It allows you to attach standardized ML model metadata to STAC items and collections, describing model architecture, the framework used, input and output specifications, training configuration, and runtime requirements. This is useful when cataloging machine learning models alongside the geospatial or scientific data they operate on.
The package is tightly integrated with the PySTAC ecosystem and depends on pystac-core, pystac-ext-classification, and pystac-ext-raster. It supports multiple versions of the MLM specification (v1.0.0 through v1.4.0) and follows semantic versioning aligned to the extension spec version. The package is actively maintained, marked Production/Stable, and requires Python 3.10 or later.
Use it for
- Catalog machine learning models used in geospatial analysis alongside the satellite or raster imagery they process.
- Document model architecture, training datasets, and performance metrics within a STAC collection for reproducibility.
- Track ML model lineage and versioning when models are retrained or updated in a data pipeline.
- Integrate ML model metadata with classification and raster extensions to describe end-to-end geospatial ML workflows.
- Enable discovery of trained models by framework, input/output format, or training approach in a searchable STAC catalog.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Active maintenance (released 4 days ago), Production/Stable status, permissive Apache-2.0 license, no known vulnerabilities, and low install friction make this a safe choice. Install it if you are already using PySTAC and need to catalog machine learning models with standardized metadata. The tight coupling to pystac-core and related extensions means it integrates seamlessly into existing PySTAC workflows.
Install
pystac-ext-mlm on PyPI
Before you install
Low friction install with active maintenance. Released 4 days ago, last commit 2026-08-10, and marked Production/Stable. Requires Python >=3.10 and depends on three pystac packages (pystac-core, pystac-ext-classification, pystac-ext-raster), all of which are part of the same ecosystem.
Requires Python >=3.10; pystac-core and related extensions must be installed as runtime dependencies.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions—you can use, modify, and distribute this code freely provided you include the license notice.
Quickstart
pip install pystac-ext-mlm
from pystac_ext_mlm import MLM
# Use MLM extension to add ML model metadata to a STAC item
mlm = MLM.from_item(item)Verify before relying
- Whether the extension supports all major ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.) or a specific subset.
- Whether model versioning and lineage tracking are supported beyond the core architecture/framework fields.
- Performance characteristics when working with large STAC catalogs containing many ML model items.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagespystac-corepystac-ext-classificationpystac-ext-raster |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,981,844 / month, #2,417 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: pystac_ext_mlm-1.4.1-py3-none-any.whl
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See also pystac-ext-label · pystac-ext-raster · pystac-ext-projection · pystac-ext-classification · pystac-ext-file · pystac-ext-pointcloud · pystac-ext-table · pystac-ext-storage · pystac-ext-eo · model-index