Difference between revisions of "MLflow"
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* [http://towardsdatascience.com/empowering-spark-with-mlflow-58e6eb5d85e8 Empowering Spark with MLflow | Albert Franzi - Towards Data Science] | * [http://towardsdatascience.com/empowering-spark-with-mlflow-58e6eb5d85e8 Empowering Spark with MLflow | Albert Franzi - Towards Data Science] | ||
* [http://www.kdnuggets.com/2018/07/manage-machine-learning-lifecycle-mlflow.html Manage your Machine Learning Lifecycle with MLflow] | * [http://www.kdnuggets.com/2018/07/manage-machine-learning-lifecycle-mlflow.html Manage your Machine Learning Lifecycle with MLflow] | ||
| + | * [http://databricks.com/ Databricks] | ||
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. It currently offers three components:: | MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. It currently offers three components:: | ||
Revision as of 12:04, 16 March 2019
Youtube search... ...Google search
- MLflow.org
- MLflow | GitHub
- Empowering Spark with MLflow | Albert Franzi - Towards Data Science
- Manage your Machine Learning Lifecycle with MLflow
- Databricks
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. It currently offers three components::
- Tracking — Record and query experiments: code, data, config, and results.
- Projects — Packaging format for reproducible runs on any platform.
- Models — General format for sending models to diverse deployment tools.
