Textbook in PDF format

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, and Di Zhu 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.
Over the last few years, Machine Learning, novel Machine Learning concepts such as attention, and more recently, large language models (LLMs) have been in the news almost every day. However, very little discussion has focused on production Machine Learning, which brings Machine Learning into products and applications. Production Machine Learning covers all areas of Machine Learning beyond simply training a machine learning model. Production Machine Learning can be viewed as a combination of Machine Learning development practices and modern software development practices. Machine Learning pipelines build the foundation for production Machine Learning. Implementing and executing Machine Learning pipelines are key aspects of production Machine Learning. In the Chapter 1, we will introduce the concept of production Machine Learning. We’ll also introduce what Machine Learning pipelines are, look at their benefits, and walk through the steps of a Machine Learning pipeline.
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