---
product_id: 407908772
title: "Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples"
price: "NT$2707"
currency: TWD
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reviews_count: 10
url: https://www.desertcart.tw/products/407908772-machine-learning-engineering-with-python-manage-the-production-life-cycle
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---

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

**Price:** NT$2707
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- **What is this?** Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
- **How much does it cost?** NT$2707 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.tw](https://www.desertcart.tw/products/407908772-machine-learning-engineering-with-python-manage-the-production-life-cycle)

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## Description

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 Introduction to ML Engineering The Machine Learning Development Process From Model to Model Factory Packaging Up Deployment Patterns and Tools Scaling Up Building an Example ML Microservice Building an Extract Transform Machine Learning Use Case

Review: Pragmatic guide to ML in practice - 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.
Review: Great Book on Machine Learning Engineering - Machine Learning engineering with Python - I would highly recommended this book for intermediate level data scientist/ ML engineers who has learned the modelling skills and want to take it forward to successfully implement the solution with advanced software engineering techniques. Author rightly understands the current gap in understanding on implementation techniques in the market and addresses the same with multiple end to end example of real-time/batch/forecasting etc. Book focuses on many important areas like designing, tracking and versioning of code, model and data (data drift) using the tools needs at each stage - model training, model re-training when drift is detected, saving the feature transformation, automating hyper parameters with Optuna and HyperOpt and pipelines and packaging it properly for testing, logging and error handling. Chapter 5 : Deployment Pattern & Chapter 6 : Scaling up stood out for me where author described various implementation patterns and perform vertical/horizontal scaling. This was a new learning for me. Additionally there was great use of pictures, tables and architecture diagrams that was very helpful. Scope of Improvement : 1. Since Author focused deployment only on AWS, readers from Azure/Google Cloud may feel left out. 2. End to end examples didn't feel end to end from the perspective of code. New people coming into the field won't be able to follow end to end examples. I felt, I problem statement and detailed implementation would be a great addition in the next version.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #4,254,387 in Books ( See Top 100 in Books ) #2,329 in Database Storage & Design #3,244 in Python Programming #6,491 in Databases & Big Data |
| Customer Reviews | 4.5 out of 5 stars 21 Reviews |

## Images

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## Customer Reviews

### ⭐⭐⭐⭐⭐ Pragmatic guide to ML in practice
*by Z***N on September 1, 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.

### ⭐⭐⭐⭐ Great Book on Machine Learning Engineering
*by J***E on March 8, 2022*

Machine Learning engineering with Python - I would highly recommended this book for intermediate level data scientist/ ML engineers who has learned the modelling skills and want to take it forward to successfully implement the solution with advanced software engineering techniques. Author rightly understands the current gap in understanding on implementation techniques in the market and addresses the same with multiple end to end example of real-time/batch/forecasting etc. Book focuses on many important areas like designing, tracking and versioning of code, model and data (data drift) using the tools needs at each stage - model training, model re-training when drift is detected, saving the feature transformation, automating hyper parameters with Optuna and HyperOpt and pipelines and packaging it properly for testing, logging and error handling. Chapter 5 : Deployment Pattern & Chapter 6 : Scaling up stood out for me where author described various implementation patterns and perform vertical/horizontal scaling. This was a new learning for me. Additionally there was great use of pictures, tables and architecture diagrams that was very helpful. Scope of Improvement : 1. Since Author focused deployment only on AWS, readers from Azure/Google Cloud may feel left out. 2. End to end examples didn't feel end to end from the perspective of code. New people coming into the field won't be able to follow end to end examples. I felt, I problem statement and detailed implementation would be a great addition in the next version.

### ⭐⭐⭐⭐⭐ Covers important topics in machine learning engineering
*by A***R on January 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.

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*Product available on Desertcart Taiwan*
*Store origin: TW*
*Last updated: 2026-05-10*