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EbookBell Team
4.7
46 reviewsAuthors 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.
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
