مهندسی یادگیری ماشین با پایتون؛ مدیریت چرخه عمر تولید مدل های یادگیری ماشین با استفاده از MLOps با مثال های کاربردی
قیمت 16,000 تومان
سال انتشار: 2021 | تعداد صفحات: 276 | حجم فایل: 16.12 مگابایت | زبان: انگلیسی
Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
Andrew P. McMahon
Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments
Explore hyperparameter optimization and model management tools
Learn object-oriented programming and functional programming in Python to build your own ML libraries and packages
Explore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use cases
Machine learning engineering is a thriving discipline at the interface of software development and 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 and services.
Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems.
By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant 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 insights into adapting 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 machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If you're someone who manages or wants to understand the production life cycle of these systems, you'll find this book useful. Intermediate-level knowledge of Python is necessary.
Table of Contents
Introduction to ML Engineering
The Machine Learning Development Process
From Model to Model Factory
Deployment Patterns and Tools
Building an Example ML Microservice
Building an Extract Transform Machine Learning Use Case