Read Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps By Valliappa Lakshmanan
Read Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps By Valliappa Lakshmanan
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Ebook About 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 modelsRepresent data for different ML model types, including embeddings, feature crosses, and moreChoose the right model type for specific problemsBuild a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuningDeploy scalable ML systems that you can retrain and update to reflect new dataInterpret model predictions for stakeholders and ensure models are treating users fairlyBook Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps Review :
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. There are a lot of good ideas that spark the design patterns in this book. But when the authors explained their solutions to the problem, it was much too focused on promoting technologies related to Google Cloud and Tensorflow. In almost every section, the authors "miss the forest for the trees"; the preferred trees are always specific Google sponsored technologies; the forest I was hoping for would be discussing the ideas in a technology agnostic manner. The original "design patterns" of object oriented programming were all technology agnostic, concept driven. This book is not like that at all. Read Online Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps Download Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps PDF Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps Mobi Free Reading Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps Download Free Pdf Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps PDF Online Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps Mobi Online Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps Reading Online Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps Read Online Valliappa Lakshmanan Download Valliappa Lakshmanan Valliappa Lakshmanan PDF Valliappa Lakshmanan Mobi Free Reading Valliappa Lakshmanan Download Free Pdf Valliappa Lakshmanan PDF Online Valliappa Lakshmanan Mobi Online Valliappa Lakshmanan Reading Online Valliappa LakshmananBest From CIA to APT: An Introduction to Cyber Security By Amoroso and Amoroso,Matthew Amoroso
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