Automated Machine Learning with Microsoft Azure: Build highly accurate and scalable end-to-end AI solutions with Azure AutoML
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A practical, step-by-step guide to using Microsoft’s AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language
Key FeaturesCreate, deploy, productionalize, and scale automated machine learning solutions on Microsoft AzureImprove the accuracy of your ML models through automatic data featurization and model trainingIncrease productivity in your organization by using artificial intelligence to solve common problemsBook Description
Automated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business.
Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK).
First, you’ll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You’ll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS).
Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems.
By the end of this Azure book, you’ll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect.
What you will learnUnderstand how to train classification, regression, and forecasting ML algorithms with Azure AutoMLPrepare data for Azure AutoML to ensure smooth model training and deploymentAdjust AutoML configuration settings to make your models as accurate as possibleDetermine when to use a batch-scoring solution versus a real-time scoring solutionProductionalize your AutoML and discover how to quickly deliver valueCreate real-time scoring solutions with AutoML and Azure Kubernetes ServiceTrain a large number of AutoML models at once using the AzureML Python SDKWho this book is for
Data scientists, aspiring data scientists, machine learning engineers, or anyone interested in applying artificial intelligence or machine learning in their business will find this machine learning book useful.
You need to have beginner-level knowledge of artificial intelligence and a technical background in computer science, statistics, or information technology before getting started. Familiarity with Python will help you implement the more advanced features found in the chapters, but even data analysts and SQL experts will be able to train ML models after finishing this book.
Table of ContentsIntroducing AutoMLGetting Started with Azure Machine Learning ServiceTraining Your First AutoML ModelBuilding an AutoML Regression SolutionBuilding an AutoML Classification SolutionBuilding an AutoML Forecasting SolutionUsing the Many Models Solution AcceleratorChoosing Real-Time versus Batch ScoringImplementing a Batch Scoring SolutionCreating End-to-End AutoML SolutionsImplementing a Real-Time Scoring SolutionRealizing Business Value with AutoML
ASIN : B08VJKVS4B
Publisher : Packt Publishing; 1st edition (April 23, 2021)
Publication date : April 23, 2021
Language : English
File size : 16391 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 : 340 pages
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Amazon Customer –
Azure Machine Learning – From Beginner to Full-Proficiency in One Book
5 stars. Fantastic text. I am an experienced technical professional who already had substantial experience in Azure Automated ML and I can honestly say that I have never had access to a resource as comprehensive to learning Auto ML as this book. I believe that 95% of what I have learned ‘on the job’ over the course of many months could have been learned from this book in ~2-3 weeks. The text is concise and includes step-by-step guidance to develop an end-to-end Machine Learning solution using Auto ML — from data ingestion, to model development, to scoring and deployment.In my opinion, Automated Machine Learning with Microsoft Azure, covers four topic areas (and does so extremely well). 1. Introduction to Automated ML and the Azure Machine Learning Service ecosystem — Chapters 1,2 a. Begin with learning the terminologies, technologies, and real-world drivers that are the basis for creating ML models. b. Walkthrough of the AML workspace/GUI c. Great place to start for those reading from a ‘novice’ level. 2. Building ML models w/ Automated ML — Chapters 3, 4, 5, 6, 7 a. Covers the three different problem types for Auto ML (Regression, Classification, Forecasting) b. Fantastic sense of ‘leveling up’ from chapter to chapter — from basic w/ the wizard to MMSA code only. 3. Leveraging ancillary technologies for other important data science activities — Chapters 9, 10, 11 a. Data science doesn’t happen in a vacuum! These chapters are indispensable to doing real data science. Data ingestion, model deployment, and containerized code all are necessities to actually get any business value form your machine learning work. b. These chapters should get special attention in order to really leverage you investment in data science! 4. Applying data science to business — Chapters 1, 8, 12 a. To quote this book, “Realizing business value ultimately comes down to whether your business partners choose to act on the predictions of your ML models.” There is a special emphasis on WHY and HOW machine learning helps us realize business value which elevates this book from a ‘just’ a technical manual to an incredibly insightful guide on how machine learning (through the lens of Automated Machine Learning on Azure) can create real value for business.These four topics amount to an awesome journey from learning what automated machine learning is, to real advice and instruction to leverage this technology to power business. I would recommend this book to anyone with an interest in using data science to increase business efficiency or anyone who wishes to learn the optimal way to perform machine learning activities in the Azure cloud. Well worth the read.
Kirk D. Borne –
The progression of ML topics in the book is perfect for many audiences
AutoML (Automated Machine Learning) is taking the data science and machine learning (ML) world from the realm of “auto-magic” reserved for the super-specialists in the field, and now bringing it more broadly to a wider spectrum of enterprise professionals involved with the A.I. and ML in their organizations. This is great. What’s greater is that the major cloud platform providers have a wide selection of AutoML tools, services, and capabilities to support those diverse cross-enterprise functions. Now, the icing on this cake is this book by Dennis Sawyers. This book delivers a brilliant, in-depth, and comprehensive selection of specific business-ready cloud-based ML solutions that can be learned and applied by professionals who have basic foundational knowledge but not necessarily “magician”-level expertise. (Remember Arthur C. Clarke’s great quote: “Any sufficiently advanced technology is indistinguishable from magic.”) In following and developing the solutions presented here (including code samples, screenshots, and system access instructions), beginners (including students) and established ML pros (and anyone in between) can expand their knowledge of the algorithms, techniques, and applications of ML and A.I.The progression in the presentation of topics in the book is perfect for many audiences (including ML courses): introduction, getting started, loading and transforming data, Python notebooks, your first AutoML model, regression, classification, forecasting, ensembles of models, real-time batch scoring, pipelines, end-to-end implementation and orchestration of the built solutions, creating web-accessible endpoints, and (the real prize at the end) producing real business value from all of these data-intensive algorithms, tools, and methods. Anyone who learns and applies the solutions presented here may soon qualify as an AutoML magician, well more likely as an AutoML expert in their organization. The magic is waiting here for those who reach for it.(Note: I received a complimentary e-copy of the book from the publisher.)