Building Machine Learning Pipelines

Using machine learning for products, services, and  critical business processes is quite different from using ML in an  academic or research setting—especially for recent ML graduates and  those moving from research to a commercial environment. Whether you  currently work to create products and services that use ML, or would  like to in the future, this practical book gives you a broad view of the  entire field.

Authors Robert Crowe, Hannes Hapke, Emily Caveness,  Di Zhu, and Catherine Nelson help you identify topics that you can dive  into deeper, along with reference materials and tutorials that teach  you the details. You'll learn the state of the art of machine learning  engineering, including a wide range of topics such as modeling,  deployment, and MLOps. You'll learn the basics and advanced aspects to  understand the production ML lifecycle.

This book provides four in-depth sections that cover all aspects of machine learning engineering:

• Data: collecting, labeling, validating, automation, and data preprocessing;  data feature engineering and selection; data journey and storage
• Modeling: high performance modeling; model resource management techniques; model  analysis and interoperability; neural architecture search
• Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging
• Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines