mlflow
MLflow streamlines the machine learning lifecycle by tracking experiments, packaging models, and facilitating deployment for reproducibility.
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Security score
The mlflow skill was audited on Mar 1, 2026 and we found 5 security issues across 2 threat categories, including 1 high-severity. Review the findings below before installing.
Categories Tested
Security Issues
high line 90
Curl to non-GitHub URL
SourceSKILL.md
| 90 | Run `mlflow models build-docker -m runs:/<run_id>/model -n my_image`. Then, deploy with `docker run -p 5000:8080 my_image`, and query the endpoint via `curl -d 'json data' http://localhost:5000/invoca |
low line 47
External URL reference
SourceSKILL.md
| 47 | mlflow.set_tracking_uri("http://localhost:5000") |
low line 65
External URL reference
SourceSKILL.md
| 65 | Integrate MLflow with frameworks like Scikit-learn, TensorFlow, or PyTorch by using their respective logging functions (e.g., `mlflow.sklearn.autolog()`). For cloud storage, set `MLFLOW_S3_ENDPOINT_UR |
low line 71
External URL reference
SourceSKILL.md
| 71 | mlflow.set_tracking_uri("http://localhost:5000") |
low line 90
External URL reference
SourceSKILL.md
| 90 | Run `mlflow models build-docker -m runs:/<run_id>/model -n my_image`. Then, deploy with `docker run -p 5000:8080 my_image`, and query the endpoint via `curl -d 'json data' http://localhost:5000/invoca |
Scanned on Mar 1, 2026
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