The following guide is a specialized, practical guide for scientists, engineers, and professionals working at the intersection of biotechnology and data science. As biomanufacturing becomes increasingly data-intensive, the ability to apply AI and machine learning techniques to bioprocess data is becoming essential for optimizing yield, quality, and reproducibility. This book demystifies machine learning in the context of complex biological systems, providing you with the analytical tools, domain-specific guidance, and cautionary insights needed to drive robust, scalable outcomes in real-world biotech production. • Learn to apply machine learning methods to biological process data, using techniques such as clustering, classification, and regression tailored for biomanufacturing environments. • Understand the intricacies of bioprocess data, including the challenges of sparse sampling, nonlinear relationships, and multivariate time-series typical in biological production systems. • Explore modeling strategies for upstream and downstream processes, covering fed-batch optimization, culture monitoring, purification analytics, and hybrid data–mechanistic modeling. • Gain insight into common pitfalls in bioprocess ML workflows, including data leakage, overfitting, misinterpreted validation, and improper feature selection in high-stakes production settings. • Develop scalable AI pipelines for biomanufacturing, enabling continuous learning, anomaly detection, and quality control through intelligent automation and real-time analytics. • Connect ML practices in bioprocessing to the future of data-driven biology, exploring how artificial general intelligence and predictive biology can unlock next-gen therapeutics and production innovation. Whether you are in biopharma, industrial biotech, or advanced biological manufacturing, Machine Learning for Bioprocess Data provides the deep technical foundation and critical best practices needed to excel in the age of intelligent bioproduction.