mxagar/mlflow_guide
My personal notes on how to use MLflow, compiled after following courses & tutorials, and after making personal experiences.
This guide helps machine learning engineers and data scientists effectively manage the entire lifecycle of their machine learning models. It provides practical notes on using MLflow to track experiments, compare model performance, package models for deployment, and maintain a central registry of trained models. You will learn how to organize your model development process from raw data to a deployable model.
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Use this if you are developing machine learning models and need a systematic way to track experiments, manage different model versions, and prepare them for deployment.
Not ideal if you are a beginner looking for an introduction to machine learning concepts rather than tools for managing the development process.
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Mar 14, 2024
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