End-to-End Machine Learning on MLflow

Machine Learning algorithms are helping businesses extract useful insights and valuable predictions from their data assets. However, every Machine Learning project has to go through multiple stages before it can finally generate accurate analytics. As a result, it is not unusual for data science practitioners to run into hurdles while building, training, and productionizing ML systems.

Machine Learning solutions need a system that continuously monitors and update them. Databricks, through MLflow, is making things easier in this respect for the data science community by streamlining all the steps involved in MLOps. While Delta Lake helps personnel carry out quick data preparation and feature engineering, platforms like MLflow make ML model review and governance significantly easier.

This ebook provides a detailed overview of the features and working of MLflow 2.0 – the advanced version of MLflow by Databricks. Included screenshots in the guide will help the readers easily trace each step of the process involved in ML model building and training. A quick comparison between Kubeflow and MLflow has also been drawn to highlight the appropriate usage for each platform.

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