Difference between revisions of "MLflow"
| Line 10: | Line 10: | ||
* [http://mlflow.org/ MLflow.org] | * [http://mlflow.org/ MLflow.org] | ||
* [http://github.com/mlflow/mlflow MLflow | GitHub] | * [http://github.com/mlflow/mlflow MLflow | GitHub] | ||
| + | * [http://towardsdatascience.com/empowering-spark-with-mlflow-58e6eb5d85e8 Empowering Spark with MLflow | Albert Franzi - Towards Data Science] | ||
MLflow is an open source platform for the complete machine learning lifecycle. MLflow is designed to work with any ML library, algorithm, deployment tool or language. It is very easy to add MLflow to your existing ML code so you can benefit from it immediately, and to share code using any ML library that others in your organization can run. MLflow is also an open source project that users and library developers can extend. [http://www.kdnuggets.com/2018/07/manage-machine-learning-lifecycle-mlflow.html Manage your Machine Learning Lifecycle with MLflow] | MLflow is an open source platform for the complete machine learning lifecycle. MLflow is designed to work with any ML library, algorithm, deployment tool or language. It is very easy to add MLflow to your existing ML code so you can benefit from it immediately, and to share code using any ML library that others in your organization can run. MLflow is also an open source project that users and library developers can extend. [http://www.kdnuggets.com/2018/07/manage-machine-learning-lifecycle-mlflow.html Manage your Machine Learning Lifecycle with MLflow] | ||
| + | |||
| + | MLflow provides three components: | ||
| + | |||
| + | #Tracking — Recording and querying experiments: code, data, config and results. Very useful to keep track of your modelling progress. | ||
| + | #Projects — Packaging format for reproducible runs on any platform (i.e Sagemaker). | ||
| + | #Models — General format for sending models to diverse deployment tools. | ||
Revision as of 02:18, 16 March 2019
Youtube search... ...Google search
MLflow is an open source platform for the complete machine learning lifecycle. MLflow is designed to work with any ML library, algorithm, deployment tool or language. It is very easy to add MLflow to your existing ML code so you can benefit from it immediately, and to share code using any ML library that others in your organization can run. MLflow is also an open source project that users and library developers can extend. Manage your Machine Learning Lifecycle with MLflow
MLflow provides three components:
- Tracking — Recording and querying experiments: code, data, config and results. Very useful to keep track of your modelling progress.
- Projects — Packaging format for reproducible runs on any platform (i.e Sagemaker).
- Models — General format for sending models to diverse deployment tools.
