BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using standard processes and designs

Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using standard processes and designs

Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using standard processes and designs Paperback / softback - 2021

by Andrew McMahon

Add to wish list
  • New
  • Paperback
New

Description

Paperback / softback. New. Machine learning engineering is an in-demand skill set for which finding a helpful guide can be difficult. Many companies struggle with creating standardized pipelines for taking proof-of-concept ML models to production to produce trustworthy results. This book discusses this and more to enable you to solve your business problems.
Ask the seller a question Add to wish list
A$98.14
A$18.92 Delivery to USA
Standard delivery: 14 to 21 days
More delivery options
Ships from The Saint Bookstore (Merseyside, United Kingdom)
Biblio Top Seller
The BIBLIO Guarantee

Details

  • Title Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using standard processes and designs
  • Author Andrew McMahon
  • Binding Paperback
  • Condition New
  • Pages 276
  • Volumes 1
  • Language ENG
  • Publisher Packt Publishing
  • Publication date 2021-11-05
  • Bookseller's Inventory # A9781801079259
  • ISBN 9781801079259 / 1801079250
  • Weight 1.06 lbs (0.48 kg)
  • Dimensions 9.25 x 7.5 x 0.58 in (23.50 x 19.05 x 1.47 cm)
  • Category Computers - Data Base Management
  • Quantity available 10

About The Saint Bookstore Merseyside, United Kingdom

Specialising in: Academic & Scholarly, Hard To Find Titles, Textbooks
Biblio member since 2018

The Saint Bookstore specialises in hard to find titles & also offers delivery worldwide for reasonable rates.

Terms of Sale: Refunds or Returns: A full refund of the price paid will be given if returned within 30 days in undamaged condition. If the product is faulty, we may send a replacement.

Browse books from The Saint Bookstore

Reader reviews for Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using standard processes and designs

Write a review for this book

There are no reviews for this book yet. Be the first to write a review!

Your review will be anonymous unless you login or create an account first.

Overall rating
Remember: You’re rating the book as a work, not the seller or the condition of the specific copy you purchased!

Important Terms and Guidelines

  • Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
  • Reviews must be at least 140 characters in length.
  • Please do not reveal critical plot elements.
  • This is not a help line. Contact customer support if you need help.
Your review must not include:
  • Obscenities, discriminatory language, or other insulting language not suitable for public domain
  • Advertisements, “spam” content, or references to other products, offers or websites.
  • Email addresses, URLs, phone numbers, physical addresses or other contact information.
  • Overly critical comments about other reviews or reviewers
  • Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
  • Availability, price, or alternative ordering/shipping information

From the publisher

Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments

Key Features
  • 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
Book Description

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
  1. Introduction to ML Engineering
  2. The Machine Learning Development Process
  3. From Model to Model Factory
  4. Packaging Up
  5. Deployment Patterns and Tools
  6. Scaling Up
  7. Building an Example ML Microservice
  8. Building an Extract Transform Machine Learning Use Case

Please wait a moment while we load the requested page.

tracking-