Difference between revisions of "MLflow"

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* [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]
 
* [http://towardsdatascience.com/empowering-spark-with-mlflow-58e6eb5d85e8 Empowering Spark with MLflow | Albert Franzi - Towards Data Science]
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* [http://www.kdnuggets.com/2018/07/manage-machine-learning-lifecycle-mlflow.html Manage your Machine Learning Lifecycle with MLflow]
  
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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]
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MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. It currently offers three components::
  
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MLflow provides three components:
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#Tracking — Record and query experiments: code, data, config, and results.
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#Projects — Packaging format for reproducible runs on any platform.
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#Tracking — Recording and querying experiments: code, data, config and results. Very useful to keep track of your modelling progress.
 
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#Projects — Packaging format for reproducible runs on any platform (i.e Sagemaker).
 
 
#Models — General format for sending models to diverse deployment tools.
 
#Models — General format for sending models to diverse deployment tools.
  
  
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http://hmkcode.github.io/images/ai/backpropagation.png
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http://databricks.com/wp-content/uploads/2018/06/mlflow.png
  
  
 
<youtube>wb-ZxtIwSTA</youtube>
 
<youtube>wb-ZxtIwSTA</youtube>
 
<youtube>ek4mJnDw8eE</youtube>
 
<youtube>ek4mJnDw8eE</youtube>

Revision as of 02:26, 16 March 2019

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MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. It currently offers three components::

  1. Tracking — Record and query experiments: code, data, config, and results.
  2. Projects — Packaging format for reproducible runs on any platform.
  3. Models — General format for sending models to diverse deployment tools.


mlflow.png


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