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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

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Amazon verified reviews

MenInSpats Dec 15, 2021

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

Amazon Verified review

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.

zeroKelvin Sep 01, 2023

There are a lot of books out there that walk you through the steps of putting together a complex ML model using ideal data in a closed setting. This is not one of those books. ML engineering with Python is instead a comprehensive guide to the way machine learning works in practice at most companies.The book does a great job of explaining the MLops tools that almost all businesses today rely on to train, deploy, serve, and iterate on models. In my opinion, the concepts in this book are far more valuable than understanding how to use specific ML frameworks to solve problems. Simply understanding that these tools exist, and knowing how they are used will give engineers a leg up, and lead to more revenue generating impact than any gold medal kaggle model could produce on its own.

Helena Dec 12, 2021

This book is for everyone interested in data related projects. It shows you the complete lifecycle of a project from business understanding up to getting the solution into production. Also, it offers you practical and easy to follow examples for each step of MLEng and MLOps leveraging the latest tools in the market. A must-have!

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

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