Machine Learning Model Serving Patterns and Best Practices

This book, "Machine Learning Model Serving Patterns and Best Practices," will guide you through the process of deploying and maintaining machine learning models in production. You will learn everything from essential model serving concepts to advanced techniques such as monitoring and optimization strategies.

What this Book will help me do

• Gain insights into key model serving patterns for effective and robust deployments.
• Apply stateful and stateless serving techniques to handle different ML workloads.
• Understand advanced patterns like ensemble and batch model serving approaches.
• Master practical tools like TensorFlow Serving, BentoML, and Ray Serve.
• Leverage AWS SageMaker for fully managed cloud-scale model serving solutions.

Author(s)

Md Johirul Islam is an experienced machine learning engineer with a deep expertise in model deployment and productionizing ML workflows. Through his research and practice, he focuses on bridging the gap between data science and production environments, ensuring reliable and scalable solutions.

Who is it for?

This book is aimed at machine learning engineers and data scientists who are familiar with ML concepts and are looking to explore model serving techniques to bring their models into production. Readers should have prior experience programming in Python and an interest in applying machine learning in real-world production scenarios.