Part 2:Diving in and building AI/ML solutions

At this point, we have equipped ourselves with the prerequisite knowledge to start building AI/ML solutions on Google Cloud. Therefore, this part focuses on actually building those solutions. We begin this part by exploring Google Cloud’s high-level AI services. These are the services you can use without requiring any expertise in AI/ML. We then move on to building custom ML models on Google Cloud. After building your first custom model, the remaining chapters in this part focus on diving deep into each step in the typical AI/ML project, such as data preparation, feature engineering, hyperparameter optimization, and deploying, monitoring, and scaling in production. We then dive into the concept of MLOps to automate all of the steps in the machine learning model development lifecycle. Next, we go beyond the technology realm and incorporate broader business and societal concepts that apply to AI/ML models, such as bias, explainability...