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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Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes
Key FeaturesMake sense of data on the cloud by implementing advanced analyticsTrain and optimize advanced deep learning models efficiently on Spark using Azure DatabricksDeploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)Book Description
The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud.
The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You’ll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You’ll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you’ll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline.
By the end of this book, you’ll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure.
What you will learnSetup your Azure Machine Learning workspace for data experimentation and visualizationPerform ETL, data preparation, and feature extraction using Azure best practicesImplement advanced feature extraction using NLP and word embeddingsTrain gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure Machine LearningUse hyperparameter tuning and Azure Automated Machine Learning to optimize your ML modelsEmploy distributed ML on GPU clusters using Horovod in Azure Machine LearningDeploy, operate and manage your ML models at scaleAutomated your end-to-end ML process as CI/CD pipelines for MLOpsWho this book is for
This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory.
Table of ContentsBuilding an End-to-end Machine Learning PipelineChoosing a Machine Learning Service in AzureData Experimentation and Visualization using AzureETL, Data Preparation and Feature ExtractionAdvanced Feature Extraction with NLPBuilding ML Models using Azure Machine LearningTraining Deep Neural Networks on AzureHyperparameter Tuning and Automated Machine LearningDistributed Machine Learning on Azure ML ClustersBuilding a Recommendation Engine in AzureDeploying and Operating Machine Learning ModelsMLOps – DevOps for Machine LearningWhat’s next?
ASIN : B07R53PCZC
Publisher : Packt Publishing; 1st edition (April 30, 2020)
Publication date : April 30, 2020
Language : English
File size : 17229 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Sticky notes : On Kindle Scribe
Print length : 539 pages
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6 reviews for 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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Si Jie –
A good ML book
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.
K Tung –
ML coverage from development to enterprise production grade deployment
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.
Jagannath Banerjee –
The most comprehensive book on Azure Machine Learning!
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 â 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, itâs a great book to buy!
Nirupam –
Book I have been waiting to have
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
Amazon Customer –
Awesome book to learn Azure ML
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.
Amazon Customer –
real fraud. book arrived in my mailbox in two days. I was so excited and spent 15min in front of the mailbox to review the content. I was shocked how authors filled (not wrote) the book with sparse, basic, and almost random pieces of info about data science. Most of the scripts are from the web. Azure stuff is very minimal and there is none on how to set up things in complex Azure environment. The language sometimes gets informal and unprofessional. Thanks to Amazon I have got my refund. Now looking up for a better book