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Data as of June 15, 2026
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MLflow
Open-source platform for the ML lifecycle including experiment tracking and model registry
AI InfrastructureFounded 2018#7 of 54 in AI Infrastructure
Updated June 15, 2026
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Company profile
What is MLflow?
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.
Latest company data
Primary data point
Package downloads (30d)Metric: npm downloadsSource: npm registryScope: UTC activity dayUpdates: DailyFreshness: 48h target; 36h expected source lagConfidence: HighCaveat: Includes all package installations including CI/CD pipelines and mirrors.Methodology →Metric: PyPI downloadsSource: PyPI BigQueryScope: UTC activity dayUpdates: DailyFreshness: 48h target; 48h expected source lagConfidence: HighCaveat: Includes all package installations including CI/CD pipelines and mirrors; pypistats is fallback only.Methodology →
37.0M/30d
- npm
- 16.0K
- PyPI
- 36.9M
Tracked packages: npm mlflow; PyPI mlflow
▲ +8%npm + PyPI: Updated 1d ago
Other data points
Dependent projectsMetric: npm dependentsSource: Libraries.io APIScope: UTC daily snapshotUpdates: DailyFreshness: 48h target; 48h expected source lagConfidence: HighCaveat: Counts direct and dev dependents.Methodology →Metric: PyPI dependentsSource: Libraries.io APIScope: UTC daily snapshotUpdates: DailyFreshness: 48h target; 48h expected source lagConfidence: HighCaveat: Counts direct reverse dependencies from PyPI.Methodology →
1.3K
- npm
- 6
- PyPI
- 1.3K
Projects depending on tracked packages: npm mlflow; PyPI mlflow
npm + PyPI: Updated 16h ago
GitHub starsMetric: GitHub starsSource: GitHub GraphQL + GH ArchiveScope: UTC daily snapshotUpdates: DailyFreshness: 48h target; 24h expected source lagConfidence: HighCaveat: Stars are bookmarks, not usage.Methodology →
26.5K
Main repository stars
Updated 16h ago
Hacker NewsMetric: Hacker News mentionsSource: Hacker News Algolia APIScope: UTC activity dayUpdates: DailyFreshness: 48h target; 24h expected source lagConfidence: HighCaveat: Search relevance and historical availability come from Algolia's HN index.Methodology →
7/30d
Position #27 in category discussion
Updated 16h ago
Repository health
Maintenance data from the main open-source repository.
OpenSSF ScorecardSource: OpenSSF ScorecardUpdates: WeeklyCaveat: Score 0–10 across security practices; not all checks apply to every repoMethodology →
5.5
Key-person riskSource: GitHub public event dataUpdates: WeeklyCaveat: Minimum developers needed for half of recent code changes. Lower = higher concentration risk.Methodology →
2
External contributorsSource: GitHub public event dataUpdates: WeeklyCaveat: Based on push events; may undercount contributors who only open PRsMethodology →
20
% of recent contributors outside the core team
Releases (30d)Source: GitHub release dataUpdates: WeeklyCaveat: Published releases in the last 30 daysMethodology →
0
Repository usage
Public repositories and source files importing packages tied to MLflow.
Repos importingMetric: Public GitHub repos using company packagesSource: ecosyste.ms Packages APIScope: UTC daily snapshotUpdates: DailyFreshness: 48h target; 48h expected source lagConfidence: HighCaveat: Public GitHub dependency data only; private repos and direct API use outside those packages are not visible.Methodology →
5.1K0%
About MLflow
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.
Tracked packages (2)
1 npm, 1 PyPI
mlflow
npmMain npm packagemlflow
Open-source platform for the ML lifecycle including experiment tracking and model registry
mlflow
PyPIMain PyPI packagemlflow
Open-source platform for the ML lifecycle including experiment tracking and model registry