The first few chapters cover the theory and practice of both DevOps and MLOps. One of the items covered is how to set up continuous integration and continuous delivery. Another critical topic is Kaizen, i.e., the idea of continuous improvement in everything. There are three chapters on cloud computing that cover AWS, Azure, and GCP. Alfredo, a developer advocate for Microsoft, is an ideal source of knowledge for MLOps on the Azure platform. Likewise, Noah has spent years getting students trained on cloud computing and working with the education arms of Google, AWS, and Azure.

One of the biggest promises of machine learning was that it would make things easier by computerizing human cognition. More enterprises are implementing machine learning (ML) to improve revenue and operations as they digitally transform their businesses. But with all the promise and opportunity behind ML, it can quickly make life harder for the teams tasked with managing it in production. Across industries, organizations are using ML for all manner of processes: predicting prices, detecting fraud, classifying health risks, processing documents, preventive maintenance, and more. Models are trained and evaluated on historical data until they appear to fit targets for performance and accuracy.