An introduction to MLOps
MLOps is an extension of the DevOps concept from the software development industry but with a specific focus on managing and automating ML model and project lifecycles. It goes beyond the tactical steps required to create machine learning models and addresses requirements that come to light when companies need to manage the entire lifecycle of data science use cases at scale. This is a good time to reflect on the ML model lifecycle stages we outlined in previous chapters in this book, as depicted in Figure 11.1.
Figure 11.1: Data science lifecycle stages
At a high level, MLOps aims to automate all of the various steps in the ML model lifecycle, such as data collection, data cleaning and preprocessing, model training, model evaluation, model deployment, and model monitoring. As such, it can be seen as a type of engineering culture that aims to unify ML system development and ML system day-to-day operations. This includes a practice...
