Learn Azure in a Month of Lunches
Article Index
Learn Azure in a Month of Lunches
Parts 2 & 3
Part 4, Conclusion

Part 4 The Cool Stuff

This section aims to give a taste of the many exciting miscellaneous features available in Azure.

The section opens with a chapter on Machine Learning (ML) and Artificial Intelligence (AI). There’s a brief overview of both AI and ML, where ML is an algorithm that learns from experience, typically looking at the probability that a certain pattern is true. There’s a useful overview of Azure Cognitive Services, which covers components such as Vision, Speech, Language, and Search. The chapter ends with a walkthrough on building an intelligent bot.

Next, Azure Automation is examined. Automation typically improves performance, standardisation and reduces the occurrence of errors. The chapter shows how to automatically install applications and configure servers using runbooks and the Power Shell Desired State Configuration tool.

The section continues with a look at Containers, these provide a level of abstraction, removing concern about the virtual hardware and OS, allowing you to concentrate on building and running your applications. Various types of container are examined.

The Internet of Things (IoT) is a hot media topic. It involves devices being connected to the internet, which facilitates additional functionality (e.g. reporting status in real-time). This chapter discusses the Azure IoT Hub, to centrally mange and collect data from various devices. There’s an instructive walkthrough on creating a simulated Raspberry Pi device (illustrating you don’t need an actual device to get started with this functionality).

This section ends with a look at Serverless computing. The idea here is that instead of running a large application on your servers, various small application components are run on a serverless computer provider (e.g. Azure Function Apps). There are servers of course, but from your viewpoint, you’re running small blocks of code on various providers. Azure’s main serverless compute features, Azure Logic Apps and Azure Function Apps, are discussed with examples.

The book ends, with a subsection titled “Don’t Stop Learning”. This is very appropriate since Azure is constantly changing, with new features being added regularly, existing features changing, and some features merging into other or becoming obsolete. There are some very helpful pointers on where to go to get additional information, courses/exams you might like to follow, and the wealth of information available on GitHub.

This section provides a very useful look at some of the many Azure features available, covering many diverse aspects of computing. I particularly enjoyed the subsection on where to go for more information.

Conclusion

This book aims to teach you Azure in around 20 hours, and succeeds - with some reservations. It will take maybe twice as long if you follow all the practicals suggested. Even longer if you run into Linux problems.

Overall, this book is mostly easy to read, having good explanations and flow between sections, helpful diagrams, and useful website links for further information. A gentle degree of humour is given that assists the reading.

The author acknowledges the screenshots and some features may get out of date quickly, owing to the rapid change in Azure’s features. Several of the exercises given in the book gave me problems, searching the internet showed others had problems too. The initial chapters were frustrating for me, I wonder how many people will give up in exasperation?! Which is a pity, because the later chapters are less troublesome. Perhaps Microsoft should provide a stable sandboxed Azure learning area that authors can use to teach others?

That said, if you’re persistent (which probably means every developer!), you will cover a lot of ground in this book and be rewarded accordingly. I especially liked the inclusion of a subsection on “Don’t Stop Learning”, with some very useful pointers for further and deeper investigation.

Regular readers will be aware this review has considerable overlap with my review of Learning Amazon Web Services in a Month of Lunches, this is because both AWS and Azure offer similar functionality, and both change rapidly enough to make following the book’s practical exercises very frustrating. I do wonder if this approach is the correct one, I suspect having online learning with the latest screenshots would be more appropriate.

So, I’m a bit torn on how to rate this book. If you get problems with the earlier chapters, you might easily give up, hence a rating of 1. If you can get over these problems (or ignore them?), this book offers a great introduction to Azure and deserves a 4.

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Grokking Machine Learning

Author: Luis G. Serrano
Publisher: Manning
Date: December 2021
Pages: 512
ISBN: 978-1617295911
Print: 1617295914
Kindle: B09LK7KBSL
Audience: Python developers interested in machine learning
Rating: 5
Reviewer: Mike James
Another book on machine learning - surely we have enough by now?



The Art of Computer Programming, Volume 4, Fascicle 5

Author: Donald Knuth
Publisher: Addison-Wesley
Pages: 320
ISBN: 978-0134671796
Print: 0134671791
Audience: Knuth fans
Rating: 4
Reviewer: Mike James
Another portion of TAoCP. Do you need to read it?


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Last Updated ( Saturday, 28 November 2020 )