Chapter 1, AI Cloud Foundations, introduces readers to the Microsoft Azure cloud and the reasons for choosing it as a platform for AI projects. We also describe the important services available to users looking to build AI solutions. This chapter also describes a decision flowchart to help pick and choose the right services on Azure that fit the business needs of an AI project.
Chapter 2, Data Science Process, focuses on the frameworks available for data science projects in a structured and organized manner. We will look at the principles of Team Data Science Process (TDSP) and the utilities available to support it. This chapter goes into the details of each step and helps define the criteria for success at every stage of the process.
Chapter 3, Cognitive Services, covers Cognitive Services in Azure, which makes it quick and simple to build smart applications. We will take a deep dive at some of the API that can be used to build AI applications without being a machine learning expert.
Chapter 4, Bot Framework, explains how to build bots using bot-related services in Azure. We will go through these options in a step-by-step manner to help you get started quickly.
Chapter 5, Azure Machine Learning Studio, explores Azure Machine Learning Studio and its advantages, and shows how we can build experiments in Azure Machine Learning Studio.
Chapter 6, Scalable Computing for Data Science, covers the vertical and horizontal scaling options in Azure to leverage cloud computing.
Chapter 7, Machine Learning Server, explains what the Microsoft Machine Learning Server is and also looks at key parts of the R and Python architecture.
Chapter 8, HDInsight, covers various functions of HDInsight in R and how to use them.
Chapter 9, Machine Learning with Spark, explains how to use Azure HDInsight in Spark, and explains what machine learning with Azure Databricks is like.
Chapter 10, Building Deep Learning Solutions, executes the steps of the popular open source deep learning tool, TensorFlow, on an Azure deep learning VM, and also covers the features of Azure Notebooks. The chapter also highlights the utilization of other deep learning frameworks, such as Keras, Pytorch, Caffe, Theano, and Chainer, using AI tools for Visual Studio/VS code and specifies deeper insights.
Chapter 11, Integration with Other Azure Services, covers typical integration patterns with other non-AI services in Azure. The reader will gain a deeper understanding of the options and best practices for integrating with functions, ADLA, and logic apps in AI solutions.
Chapter 12, End-to-End Machine Learning, explains how to get started with Azure Machine Learning services for end-to-end custom machine learning.
