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Tracking experiments to record and compare parameters and results (MLflow Tracking). Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production (MLflow Projects). Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (MLflow Models). Providing a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations (MLflow Model Registry). MLflow is library-agnostic. You can use it with any machine learning library, and in any programming language, since all functions are accessible through a REST API and CLI. For convenience, the project also includes a Python API, R API, and Java API.
(more details... https://mlflow.org/docs/latest/index.html)
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1. Make Python3 default
Python2 and Python3 are already installed on Debian 10, but the default version is Python2. How to check it...
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