Building Machine Learning Pipelines

Building Machine Learning Pipelines guides readers through the intricate process of automating model life cycles with TensorFlow. Ideal for data scientists and ML engineers, this book is structured around practical implementation and real-world examples, such as orchestrating TFX pipelines with Apache Beam and Kubeflow.

What You'll Learn

• Implement and automate ML pipelines using TensorFlow
• Use Apache Beam for data processing and orchestration
• Conduct model analysis for fairness and distribution strategies
• Deploy scalable solutions with Kubernetes and TensorFlow Serving
• Understand data validation and privacy with TensorFlow Privacy

Who This Book Is For

This book is perfect for data scientists, machine learning engineers, and software developers who are involved in building and deploying machine learning models. It assumes a foundational understanding of machine learning principles and TensorFlow.

What You Need to Know First

Familiarity with machine learning concepts and TensorFlow is recommended. Basic knowledge of Python programming and cloud platforms such as GCP will be beneficial.

Why This Book

Unlike other resources, this book provides a hands-on approach to building ML pipelines with clear, step-by-step instructions and real-world examples. It focuses on practical solutions rather than theoretical discussions, making it a valuable resource for professionals.

What's Inside

Authored by Hannes Hapke and Catherine Nelson, published by O'Reilly Media, Inc., this book spans 367 pages. It includes comprehensive chapters on setting up TensorFlow Serving, conducting model A/B testing, and deploying applications to Kubernetes.