Frameworks for Machine Learning Model Management - inovex-Blog

In my previous blog post „how to manage machine learning models" I explained the difficulties within the process of developing a good machine learning model and motivated using a tool to support data scientists with this challenge. First there will be one paragraph per framework that describes the project and shows some code examples. In the end of the article you will find a framework comparison and recommendations when to use which framework. As with my previous post the sklearn dataset on Boston-Housing prices will be used as basis. You can find a notebook to play with in this github repo. This notebook also includes instructions how to install the frameworks as well as some other functions we will use within the code examples below, but that won't be discussed further, to place focus on the framework specific parts and omit boilerplate code. DVC means „data (science) version control" and aims to do for data science what git already does for software development: Making development processes traceable and reproducible.