Strongest current signal
Dependents
1.2K
Known dependents across npm and PyPI
Open-source platform for the ML lifecycle including experiment tracking and model registry
Best current coverage: 1.2K dependents, 5.1K public repos, and 2 contributors.
Lead signals
Package pull and public code usage both show up clearly for MLflow.
Strongest current signal
1.2K
Known dependents across npm and PyPI
2
GitHub contributors in the last 30 days
25.3K
Main repository stars
7/30d
Ranked #28 in category discussion
Research Brief
No recent Research Brief centers MLflow yet. Start with the latest market reporting, then return here for the stored company signals on this page.
Browse Research Briefs →Reading
existing public code and downstream usage are the clearest current signals for MLflow.
Existing public code
5.1K
Public repositories importing tracked packages
Downstream usage
1.2K
Known dependent packages across both registries
Engineering activity
2
GitHub contributors active in the last 30 days
GitHub attention
25.3K
Main repository stars
Coverage includes npm and PyPI registries, GitHub, public code import detection, developer discussion, distribution surfaces, and recent company news where available. As of April 14, 2026. Methodology
Sustainability and maintenance signals from the primary public repository.
These signals come from public code import detection and tracked hiring posts rather than registry totals alone.
Public repositories and source files importing packages tied to MLflow.
Background and reference details
background, categories, and tools stay collapsed until you need them.
MLflow is the industry-standard open-source platform for managing the machine learning lifecycle, including experiment tracking, reproducible runs, model packaging, and a model registry. Originally created at Databricks, it is one of the most downloaded ML packages on PyPI.
A centralized model store for collaborative lifecycle management, supporting versioning, stage transitions, and annotations for models.
Manages and deploys machine learning models from various ML libraries to diverse serving and inference platforms efficiently.
Packages ML code in a reusable and reproducible format, enabling easy sharing and execution across different platforms and environments.
Records and queries machine learning experiments, including code, data, configuration, and results, to organize and compare runs effectively.
Historical metrics for MLflow
MLflow: GitHub Stars up 101272% (25 to 25.3K). Contributors down 50% (4 to 2).
| Date | GitHub Stars | HN Mentions | Contributors |
|---|---|---|---|
| Mar 16, 2026 | 25 | 0 | 4 |
| Mar 23, 2026 | 41 | 1 | 2 |
| Mar 30, 2026 | 25.0K | 4 | 6 |
| Apr 6, 2026 | 25.2K | 1 | 3 |
| Apr 13, 2026 | 25.3K | 1 | 2 |
| Apr 14, 2026 | 25.3K | 0 | 2 |