--- product_id: 226318419 title: "Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps" price: "€ 85.64" currency: EUR in_stock: true reviews_count: 13 url: https://www.desertcart.at/products/226318419-machine-learning-design-patterns-solutions-to-common-challenges-in-data store_origin: AT region: Austria --- # Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps **Price:** € 85.64 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps - **How much does it cost?** € 85.64 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.at](https://www.desertcart.at/products/226318419-machine-learning-design-patterns-solutions-to-common-challenges-in-data) ## 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 The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly Review: A useful book for a n00b like me with a background in programming - My background: I'm an expert software engineer (C++, Java, etc) and proud n00b at machine learning. I've read the O'Reilly "AI and Machine Learning for Coders" book and many online articles. I have a background in trading/financial software, which exposed me to many statistical terms in this book. In the past, PhD level physics/math quants would typically handle those topics and this book has helped me realize some gaps in my knowledge and fill them (sometimes via online search). I can now at least reason about those concepts better even if I don't yet understand the details. I'm 1/3 into the book (so maybe premature for 5 stars) and it's been a dense but interesting read so far. There have been times where I have to lookup terms but the material has still been approachable. The language in the first couple chapters could probably be simplified some but it was sufficient for me with a lot of coffee. I expect to still have very incomplete knowledge after finishing this book due to lack of practical experience. However, my goal is to build a large scaffolding of knowledge/concepts on ML that I can use as a foundation for future learning and broaden my toolbox before I start hacking code. When I was learning C++, I found the Gang of Four book "Design Patterns" accomplished a similar goal to help bridge the gap between academic knowledge and practical software engineering. Much like with the GoF book I suspect I may be re-reading parts of this book in the future when my knowledge has matured. Some may prefer doing a lot of ML coding before reading this book, but I like to have a lot of background knowledge/tools before tackling code - personal preference I guess. I seem to have discovered an error/typo regarding "precision" vs "recall" in chapter 3: Page 135 paragraph 2: "If we care more that our model is correct whenever it makes a positive class prediction we'd optimize our prediction threshold for recall". I think the last word in that sentence should be "precision". The terms are defined on page 124 paragraph 2. Review: Good to fill gaps in knowledge and become aware of many things that are done subconsciously - I thought this was a great book for providing people with an understanding of the toolkit that ML engineers need to know when making Machine Learning models. As a side note, I bought this to be better prepared for ML architecture and design interviews. If you are in a hurry, I think the content in Chapters 2, 3, and 4 are great. 5 was somewhat relevant for me and Chapters 6, and 7 are not really relevant until you are actually neck-deep in the models, so they did not really apply to me. Chapter 8 was fantastic since it had a Common Patterns by Use Case and Data Type section, and enumerated many different types of problems and the tools that one might use to tackle them. I am satisfied with what I got from this book. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #682,497 in Books ( See Top 100 in Books ) #91 in Machine Theory (Books) #132 in Business Intelligence Tools #1,231 in Artificial Intelligence & Semantics | | Customer Reviews | 4.6 out of 5 stars 411 Reviews | ## Images ![Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps - Image 1](https://m.media-amazon.com/images/I/81Gw--pXkzL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ A useful book for a n00b like me with a background in programming *by J***O on December 17, 2020* My background: I'm an expert software engineer (C++, Java, etc) and proud n00b at machine learning. I've read the O'Reilly "AI and Machine Learning for Coders" book and many online articles. I have a background in trading/financial software, which exposed me to many statistical terms in this book. In the past, PhD level physics/math quants would typically handle those topics and this book has helped me realize some gaps in my knowledge and fill them (sometimes via online search). I can now at least reason about those concepts better even if I don't yet understand the details. I'm 1/3 into the book (so maybe premature for 5 stars) and it's been a dense but interesting read so far. There have been times where I have to lookup terms but the material has still been approachable. The language in the first couple chapters could probably be simplified some but it was sufficient for me with a lot of coffee. I expect to still have very incomplete knowledge after finishing this book due to lack of practical experience. However, my goal is to build a large scaffolding of knowledge/concepts on ML that I can use as a foundation for future learning and broaden my toolbox before I start hacking code. When I was learning C++, I found the Gang of Four book "Design Patterns" accomplished a similar goal to help bridge the gap between academic knowledge and practical software engineering. Much like with the GoF book I suspect I may be re-reading parts of this book in the future when my knowledge has matured. Some may prefer doing a lot of ML coding before reading this book, but I like to have a lot of background knowledge/tools before tackling code - personal preference I guess. I seem to have discovered an error/typo regarding "precision" vs "recall" in chapter 3: Page 135 paragraph 2: "If we care more that our model is correct whenever it makes a positive class prediction we'd optimize our prediction threshold for recall". I think the last word in that sentence should be "precision". The terms are defined on page 124 paragraph 2. ### ⭐⭐⭐⭐⭐ Good to fill gaps in knowledge and become aware of many things that are done subconsciously *by E***E on July 17, 2021* I thought this was a great book for providing people with an understanding of the toolkit that ML engineers need to know when making Machine Learning models. As a side note, I bought this to be better prepared for ML architecture and design interviews. If you are in a hurry, I think the content in Chapters 2, 3, and 4 are great. 5 was somewhat relevant for me and Chapters 6, and 7 are not really relevant until you are actually neck-deep in the models, so they did not really apply to me. Chapter 8 was fantastic since it had a Common Patterns by Use Case and Data Type section, and enumerated many different types of problems and the tools that one might use to tackle them. I am satisfied with what I got from this book. ### ⭐⭐⭐⭐ Has some helpful uses *by S***U on August 6, 2023* The books is mostly from a computer science prospective. I am from an engineering background so my review may be biased. It covers design patters for data treatment, model design to MLOPs. I like the first two sections and my review is based on them. It provides alternative design patterns that I did not know before and they are purely practical. No theories involved ## Frequently Bought Together - Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps - Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications - Machine Learning System Design Interview --- ## 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.at/products/226318419-machine-learning-design-patterns-solutions-to-common-challenges-in-data](https://www.desertcart.at/products/226318419-machine-learning-design-patterns-solutions-to-common-challenges-in-data) --- *Product available on Desertcart Austria* *Store origin: AT* *Last updated: 2026-06-02*