What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Key benefits

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

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.

Who is this book 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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Machine Learning Engineering with Python

₱2959.99

₱2857.99

₱2500.99

Total ₱ 8,318.97

FAQs

How do I buy and download an eBook?

Where there is an eBook version of a title available, you can buy it from the book details for that title. Add either the standalone eBook or the eBook and print book bundle to your shopping cart. Your eBook will show in your cart as a product on its own. After completing checkout and payment in the normal way, you will receive your receipt on the screen containing a link to a personalised PDF download file. This link will remain active for 30 days. You can download backup copies of the file by logging in to your account at any time.

If you already have Adobe reader installed, then clicking on the link will download and open the PDF file directly. If you don't, then save the PDF file on your machine and download the Reader to view it.

Please Note: Packt eBooks are non-returnable and non-refundable.

Packt eBook and Licensing When you buy an eBook from Packt Publishing, completing your purchase means you accept the terms of our licence agreement. Please read the full text of the agreement. In it we have tried to balance the need for the ebook to be usable for you the reader with our needs to protect the rights of us as Publishers and of our authors. In summary, the agreement says:

You may make copies of your eBook for your own use onto any machine
You may not pass copies of the eBook on to anyone else

How can I make a purchase on your website?

If you want to purchase a video course, eBook or Bundle (Print+eBook) please follow below steps:

Register on our website using your email address and the password.
Search for the title by name or ISBN using the search option.
Select the title you want to purchase.
Choose the format you wish to purchase the title in; if you order the Print Book, you get a free eBook copy of the same title.
Proceed with the checkout process (payment to be made using Credit Card, Debit Cart, or PayPal)

Where can I access support around an eBook?

If you experience a problem with using or installing Adobe Reader, the contact Adobe directly.
To view the errata for the book, see www.packtpub.com/support and view the pages for the title you have.
To view your account details or to download a new copy of the book go to www.packtpub.com/account
To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us

What eBook formats do Packt support?

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks?

You can get the information you need immediately
You can easily take them with you on a laptop
You can download them an unlimited number of times
You can print them out
They are copy-paste enabled
They are searchable
There is no password protection
They are lower price than print
They save resources and space

What is an eBook?

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.

Filter reviews by

All

Amazon verified reviews

Del Middlemiss Dec 01, 2021

I have been working my way through this book for the last couple of weeks. It's very well written, the examples are practical and realistic, and the advice is very useful. It's an up to date take on a rapidly evolving field. Highly recommended!

Amazon Verified review

Amazon Customer Jan 14, 2022

This book will help you fill the gaps in your understanding of machine learning engineering and machine learning development process. Models in production constantly suffer from data drift, from the need to retrain and maintain the models in the pipelines. The authors provide a comprehensive overview of the modern approaches and give examples of real life solutions. You will find examples with Apache Spark and serverless architecture as well as AWS.What I liked the most was the dataset and code examples in the github repo that goes together with the book. The examples are given in the python notebook files, starting from simple solutions as detecting anomalies and to specific and more narrow examples of how to continuously retrain a model in the serverless cloud.This book will definitely be interesting for engineers who start deploying their models in production and want to make this process work the best way for their business.

MenInSpats Dec 15, 2021

Lots of great insights, clearly explained and with very practical examples. Highly recommended.

Vincent Boucher Feb 06, 2022

"Machine Learning Engineering with Python"Packt was kind enough to send me a complimentary copy of the book. Here's my balanced review:"Machine Learning Engineering with Python" is an accessible and timely clear-minded machine learning book with easy to deploy practical end-to-end examples. The book is comprehensive and the concepts are presented in a way that they can be deployed using the classic machine learning tools.Machine Learning Engineering is an increasingly important approach underlying the deployment ML products and services. I recommended "Machine Learning Engineering with Python" to developers working with artificial intelligence and Python.#Engineering #MachineLearning #Python

Marco Carnini Mar 26, 2022

This book is mostly for Machine Learning specialist that want to be more actively (and useful) contributors to the projects. The full Data Science pipeline is considered, from data ingestion to design and deployment and the solution.Way too often, Data Science projects failed or are delayed because of the difficult communications between people caring about the model training and little to the business value or the deployment.This book help filling this gap by training the read to properly design the solution upfront, how to split the task inside the team, how to efficiently delivered and deploy on cloud. While the focus is on AWS, the recipes can be easily transferred to other cloud solutions.I particularly appreciated the global view of Data Science and normal software development practices. Way too often I saw that Agile methodologies (or Lean, like Kanban) can not be applied to Data Science. This book proves otherwise. Similarly, there are references about versioning the models. The exposition is not fully exhaustive (and I doubt this is actually possible): agile methodologies are referenced quickly, as well as git and MLflow are not extensively illustrated. This would be detrimental for the book, that would become an arid, boring and verbose treaty.With this book, you get a general framework to introduce model software engineering best practices in the pipeline. With the two use cases presented in depth as the last two chapters, the author manage to provide a pragmatic, synthetic view. The use cases are relevant, and not abstract, academia-like projects.The reading is quite pleasant. But, most importantly, easy to implement for quick improvement in Machine Learning Engineering.

Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

What do you get with eBook?