This is the code repository for Machine Learning Model Serving Patterns and Best Practices, published by Packt.
A definitive guide to deploying, monitoring, and providing accessibility to ML models in production
Serving patterns enable data science and ML teams to bring their models to production. Most ML models are not deployed for consumers, so ML engineers need to know the critical steps for how to serve an ML model.
This book will cover the whole process, from the basic concepts like stateful and stateless serving to the advantages and challenges of each. Batch, real-time, and continuous model serving techniques will also be covered in detail. Later chapters will give detailed examples of keyed prediction techniques and ensemble patterns. Valuable associated technologies like TensorFlow severing, BentoML, and RayServe will also be discussed, making sure that you have a good understanding of the most important methods and techniques in model serving. Later, you’ll cover topics such as monitoring and performance optimization, as well as strategies for managing model drift and handling updates and versioning. The book will provide practical guidance and best practices for ensuring that your model serving pipeline is robust, scalable, and reliable. Additionally, this book will explore the use of cloud-based platforms and services for model serving using AWS SageMaker with the help of detailed examples.
By the end of this book, you'll be able to save and serve your model using state-of-the-art techniques.
This book covers the following exciting features:
- Explore specific patterns in model serving that are crucial for every data science professional
- Understand how to serve machine learning models using different techniques
- Discover the various approaches to stateless serving
- Implement advanced techniques for batch and streaming model serving
- Get to grips with the fundamental concepts in continued model evaluation
- Serve machine learning models using a fully managed AWS Sagemaker cloud solution
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
test_images = test_images.astype(np.float32) / 255.0
Following is what you need for this book:
This book is for machine learning engineers and data scientists who want to bring their models into production. Those who are familiar with machine learning and have experience of using machine learning techniques but are looking for options and strategies to bring their models to production will find great value in this book. Working knowledge of Python programming is a must to get started.
With the following software and hardware list you can run all code files present in the book (Chapter 1-15).
| Chapter | Software required | OS required |
|---|---|---|
| 1-15 | PostMan | Any OS |
| 1-15 | Flask | Any OS |
| 1-15 | TensorFlow | Any OS |
| 1-15 | Ray serve | Any OS |
| 1-15 | BentoML | Any OS |
| 1-15 | Apache AirFlow | Any OS |
| 1-15 | Python 3.6 | Any OS |
Md Johirul Islam is a data scientist and engineer at Amazon. He has a PhD in Computer Science and is also an adjunct professor at Purdue University. His expertise is focused on designing explainable, maintainable, and robust data science pipeline applying the software design principles and helping organizations deploy machine learning models into production at scale.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.