In this part of the series of articles on MLOps, we start with information that will be familiar to most of you: With the basics of Git. However, to give a different perspective on the well-known tool, these basics provide the basis to highlight the function and benefits of Git for machine learning (ML) and the difference in managing training data.

With Machine Learning Operations (MLOps) we ensure that data is efficiently and strategically integrated into business processes through regular and automated training, thus contributing to increased revenue. The challenge is to establish and maintain these automated processes.

So far, we have generally steered clear of the areas of text comprehension and text generation by ML in our practical examples for the basic understanding of AI. For good reason, we have focused primarily on two types of problems: classification of images and prediction of numerical values.