Part 2: Building Blocks and Patterns for a Next-Generation AI Ecosystem
This part of the book provides a comprehensive exploration of big data systems and AI/ML workflows, emphasizing privacy management, model design pipelines, and life cycle management. It covers various stages of the machine learning pipeline, model evaluation, and handling uncertainty, and addresses common challenges. Additionally, it discusses advanced topics such as hyperparameter tuning, MLOps practices, and AutoML. By offering theoretical discussions and practical guidance, this part equips you with the knowledge and tools to navigate the complex landscape of deploying AI models on top of big data systems while ensuring robust, efficient, and privacy-preserving solutions.
This part is made up of the following chapters: