Machine Learning Engineering with Python

Supercharge your machine learning models to scale them to larger training and prediction data sets

Key Features

• Explore hyperparameter optimization and model management tools
• Learn about object-orientated, functional, and user-defined libraries and discover how these relate to ML solution development and deployment
• Work with examples of different types of ML solutions using popular tools and patterns

Book Description

Machine learning engineering is a growing discipline that involves building robust software components by leveraging machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products.

Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential concepts, implementation, and associated methodologies to have you up and running and productive in no time using practical examples. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations. This book will then walk you through the steps needed to build and deploy machine learning solutions. As you advance, you'll explore ways to build your own toolsets for training and deployment in consistent ways across all your projects. The book also provides practical discussions of deployment architectures along with their pros and cons and ways of scaling up your solutions. Finally, you'll work through examples to help you solve typical business problems.

By the end of this machine learning book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistent performance machine learning engineering.

What you will learn

• Find out what an effective ML engineering process looks like
• Uncover options for automating training and deployment and learn how to use them
• Discover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutions
• Understand what aspects of software engineering you can bring to machine learning
• Gain practical advice on how to adapt software engineering for machine learning using appropriate cloud technologies
• Perform hyperparameter tuning in a relatively automated way

Who This Book Is For

This book is for data scientists, machine learning engineers, and software developers who want to build robust software solutions with a machine learning component to start managing the production cycle of these systems. The book assumes intermediate-level knowledge of Python.

Table of Contents

1. Introduction to ML Engineering
2. The Machine Learning Development Process
3. From Model to Model Factory
4. User Defined Libraries
5. Deployment Architectures and Tools
6. Scaling Up
7. Example 1 - User Defined Libraries for ML Workflows
8. Example 2 - ML Microservice