Open Source MLOPs

The process of deriving useful insights from machine learning can be an arduous, though rewarding, one, even for data science practitioners. It’s worth investing in any tools or techniques that can assist with the process. Open Source MLOPs with DVC and CML will take you through two such techniques, which will allow you to automate your machine learning pipelines and make them eminently reproducible. You'll begin with an introduction to Data Version Control (DVC) and learn how it can help you keep track of your machine learning artifacts using a familiar Git-like approach. This will lead you on to building end-to-end machine learning pipelines, complete with visualizations of the results. We move on to Continuous Machine Learning (CML), with which you can automate the training and testing of machine learning models so they can run alongside the rest of your CI/CD pipeline, ensuring stability and reproducibility. By the end of this book, you will be able to develop reproducible pipelines as directed acyclic graphs and run those pipelines effortlessly in the cloud to speed up the development of your machine learning models.