Jagannath Banerjee Sep 19, 2020

Mastering Azure Machine Learning - As the name aptly suggests, this book is a highly focused approach to overall life cycle of Machine Learning, Deep Learning(ANN & CNN) , Natural Language Processing (NLP) and Recommender System using Microsoft Azure as a platform. Author did an excellent job in explaining such wide subject into 400 pages with workable codes, picture and enough text that will comfortably help you to take off to your AI journey in Azure.What I really liked is the smooth flow of concepts followed by code. Everything from building a virtual machine, computation, workspace to launching machine-learning landscape is thorough. Author begins with data exploration, data preparation techniques, feature engineering, building models, metrics comparison, optimization and deployment. Author introduces us to 5 major ML landscape provided by Azure platform – Azure ML Designer, AutoML, Azure Machine Learning, Cognitive Toolkit and Databricks.I specially loved chapter 5 where we built ML workflow using pipelines that setups end-to-end process for training, scoring and re-training and chapter 12 which demonstrates ML model deployment in Azure, how to log our results and application metrics. I have read many books on Machine Learning and hardly any book captures the deployment details as nice as this book. The deployments mentioned in the book are industry standard and I was able to use the concepts in my current project.This book is not for absolute beginners in the field. Someone with 1 to 2 years of experience in the AI field with basic understanding of Azure, Python, Machine Learning and Shell Script will benefit the most. This book explains basic concepts theoretically but lacks any mathematics.Overall, it’s a great book to buy!

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K Tung Oct 06, 2020

This book covers how to build and deploy machine learning models in Microsoft Azure. The main tool or platform for user to follow along this book is Microsoft Azure Machine Learning Service, which is a platform-as-a-service (PaaS) offering by Microsoft. Authors provide guidance and suggestions about creating Azure subscription (with $200 USD credit) and the minimum compute type to work through these examples in the book. So, if your company or you already have Azure subscription, and Azure Machine Learning service is enabled in your subscription, you are all set.Authors provide many useful examples and boiler plate code to demonstrate how to leverage Azure Machine Learning Service as an end-to-end PaaS offering for data scientists and machine learning engineers in both discovery as well as deployment in Azure. Examples are pretty straightforward to follow and execute. Authors also spent enough pages to demonstrate no-code machine learning in building a matchbox recommender. For users who are new to Python (i.e., if you have been working with R or Matlab primarily), you would appreciate the section about no-code approach of building a machine learning model through Azure Machine Learning designer in Chapter 5.This book really did a justice for Azure Machine Learning Service. This book also gives enough coverage to distributed training, data pipeline, as well as model deployment to container registry. I frequently see that there is a divide between those who build models, and those who have to figure out how to serve the model. Each side view the other as a black box. This book helps demystify the gaps. In section 4, where the focus is on model deployment, it demonstrates how to refactor model training code into scoring script and implement it as a pipeline. My suggestion for this section is that it could be more helpful to readers if more of Azure dashboards could be shown, for example, where to look for scoring URL of a model from within Azure portal, and even better, if it could be shown to readers as to how one can use generic tools such as Postman to solicit RESTful API call for model scoring, that would be very helpful.Overall, this book is very helpful in covering and explaining Azure Machine Learning Service as a PaaS offering for end-to-end machine learning workflow. I think whether you are an expert machine learning scientist or a novice data scientist, you will find the examples relevant and applicable. An improvement would be to show more of Azure dashboard, especially when it comes to storing docker images, accessing scoring URL, and management of workspace in a team environment.

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Si Jie Apr 29, 2021

I'm captivated by this book. Just went through the first chapter and this is exactly what I need. Besides the Azure part, it is a pretty well-rounded ML book itself.

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Amazon Customer Nov 13, 2020

Very Nice work. Enjoyed reading the details. Very hands on book with practical examples. Would serve as helpful resource for ML workforce who uses Azure cloud.

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Nirupam Nov 23, 2020

I got this book a few weeks ago and have been amazed by both the depth and breadth of content present in the book. Some of the features I liked are:1. Book goes through different basics of services provided by Azure for data scientists and ML engineers.2. There are many chapters that cover each step of building machine learning models through Azure services for example data visualization, collection, feature engineering, pre-built APIs, ETL, modeling and deployment.3. To explain each topic, the author has given clear python code with instructions so that readers can not only replicate but also apply the code to their own work.4. Author has provided chapters on using advanced frameworks for computer vision and NLP which makes this book my goto book for everything related to ML on azure.5. My favourite topic is model deployment and MLDevOps which explain in detail how to maintain and serve the models.Close your eyes and buy this book blindly and you thank the author and reviewers for recommending this book

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Mastering Azure Machine Learning: Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning

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