Models Manager is an analytics module base on mlflow MLflow | MLflow that allow datascientics to manage their machine learning experiments and models.

MLflow is an open-source platform to manage the Machine Learning development lifecycle from start to finish, and for this purpose it includes 4 main functionalities/modules:

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• Tracking of experiments to record and compare parameters and results (MLflow Tracking).

• Packaging the ML code in a reusable and reproducible form for sharing with other data scientists and deploying it into production (MLflow Projects).

• Management and deployment of models from a wide variety of ML libraries to a variety of platforms for servicing (MLflow Models)

• Provide a central model repository to collaboratively manage the entire lifecycle of an MLflow model, including model versioning, stage transitions, and annotations (MLflow Model Registry).

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This module can be use with the notebooks of the platform and with some clients for different program languages. You can access to some examples of use in the other sections Using Models Manager from local enviroment and Using Models Manager from notebooks

Only analytics/datascientics user can use it and it can be found in Analytics Tools menu:

Inside, we can see the UI base on MLFlow tracking interface, where the user can work with different experiments and executions and transform them to productive models.

Next steps

• Full integration with Platform security: each user will see his experiments and will be able to share them with other users.

• Serving Models as Platform APIS

• Deployment of models as containers