--- product_id: 407908772 title: "Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples" price: "$98.19" currency: USD in_stock: true reviews_count: 10 url: https://www.desertcart.us/products/407908772-machine-learning-engineering-with-python-manage-the-production-life-cycle store_origin: US region: United States of America --- # Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples **Price:** $98.19 **Availability:** ✅ In Stock ## Quick Answers - **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?** $98.19 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.us](https://www.desertcart.us/products/407908772-machine-learning-engineering-with-python-manage-the-production-life-cycle) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples: 9781801079259: Computer Science Books @ desertcart.com 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 | #3,309,643 in Books ( See Top 100 in Books ) #1,402 in Database Storage & Design #2,625 in Python Programming #4,711 in Databases & Big Data | | Customer Reviews | 4.5 4.5 out of 5 stars (21) | | Dimensions | 7.5 x 0.63 x 9.25 inches | | ISBN-10 | 1801079250 | | ISBN-13 | 978-1801079259 | | Item Weight | 15.5 ounces | | Language | English | | Print length | 276 pages | | Publication date | November 5, 2021 | | Publisher | Packt Publishing | ## Images ![Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples - Image 1](https://m.media-amazon.com/images/I/61j4ewGmZrL.jpg) ## 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. --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.us/products/407908772-machine-learning-engineering-with-python-manage-the-production-life-cycle](https://www.desertcart.us/products/407908772-machine-learning-engineering-with-python-manage-the-production-life-cycle) --- *Product available on Desertcart United States of America* *Store origin: US* *Last updated: 2026-05-09*