Book Image

Hands-On Machine Learning with Azure

By : Thomas K Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak
Book Image

Hands-On Machine Learning with Azure

By: Thomas K Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak

Overview of this book

Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way. The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure. By the end of this book, you will be fully equipped to implement smart cognitive actions in your models.
Table of Contents (14 chapters)

What this book covers

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.