Last week i stumbled upon this book and this weekend there was enough time to walk through it:
The book contains 14 chapters on 409 pages - but due to the layout, i think it can fit on 200 pages on a book with "default rendering".
The book is in addition divided in 4 sections: 1 - Azure Machine Learning / 2 - Experimentation and Data Preperation / 3 - Training Machine Learning Models / 4 Optimization and Deployment of Machine Learning Models
Chapter 1 is named "Building an end-to-end machine learning pipeline in Azure". I struggled with this title, but in the first section it is explained: "You can see it as an overview of the book". The subsections cover data exploration, data preparation, choosing the model, optimization and deploying/operating models. The chapter is a teaser with many graphs, examples, stragetgies - a fast end-to-end walk through.
"Choosing a machine learning service in Azure" is the title of the second chapter. Here is everything discussed about ML vs. AI and the Azure services, which provide these techniques (e.g. Data Science Virtual Machine, Azure Batch, Azure Databricks, Azure Functions, Azure IoT Edge, Custom Vision, Azure Machine Learning Designer, Machine Learning Studio, ...). This chapter contains many screenshots and code snippets - from my point of view to much at this point.
In chapter three (Data experimentation and visualization using Azure) it is shown how to setup your environment via Azure CLI, so that you are able to perform these steps again and again for new projects. In addition it is presented how to run everything on the local machine and track the metrics and artifacts to the Azure workspace. After that visualization is explained including code examples. Pairplots, principal component analysis, quadratic discriminant analysis, stochastic neighbor embedding - Really cool.
Chapter 4 is about "ELT, data preparation and feature extraction". Here are some nice commands with Azure CLI provided: How to batch upload data up to the Azure storage accounts and attaching them to the ML workspace. And how to access this data via python.
Chapter 5 "Azure Machine Learning Pipelines" is about to make the content of chapter 4 reusable. I think nothing to note here - a nice reference for the python code which is needed.
"Advanced feature extraction with NLP" is chapter 6. NLP = natural language processing. Nothing more to say here.
The chaper 7 to 9 are about training machine learning models. I think i will not describe each of them. But here a short summary: It starts with decision trees as explanation and then does a deep dive in how to use LightGBM including the python code. Then the same for convolutional neural networks (CNN): explanation/motivation + coding. This is followed by the description of Azure Hyperdrive: tuning and optimizing the machine learning process. The concept of hyperparameters (e.g. number of neurons in a layer) is introduced and how to choose them with grid sampling on an elastic cloud infrastructure. And last but not least: it is described how Azure provides "a service to users that automatically preprocesses your data, selects an ML model, and trains and optimizes the model to optimally fit your training data [...]".
Chapter 10 is about using clusters. This is a nice introduction about partitioning data, workloads and synchronizing worker nodes.
"Building a recommendation engine in Azure" is the title of chapter 11. Just some catchwords from the content: non-personalized, contentbased, rating-based, hybrid recommendations. After this chapter you will know, why amazons recommendations are like they are ;-)
In chapter 11 & 12 it is described, how to register, deploy and operate a recommendation engine or machine learning model up to MLOps.
The book closes with chapter 14 "What's next?". Most important point like everywhere: Automation...
Summary: I liked this book very much, because every topic starts with an excellent introduction and there are many code examples, so that you can us this book as reference as well. The basic understanding of the author is best described with the following quote:
"the most important tasks [are]: Data acquisition, Data cleansing, Data labeling, Selecting an error metric. We don't want to blame anyone, but some machine learning engineers love to simply skip these topics and dive right into the fun parts, namely feature engineering, model selection, parameterization, and tuning."
That hits the bull's eye.