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)

AI Cloud Foundations

Today, every organization aspires to be a leader in adopting the latest technological advancements. The success of such adoption in recent years has been achieved by leveraging the data landscape surrounding businesses. In this chapter, we will talk about how AI can be leveraged using Microsoft's Azure platform to derive business value from that data landscape. Azure offers several hundred services, and choosing the right service is challenging. In this chapter, we will give a high-level overview of the choices a data scientist, developer, or data engineer has for building and deploying AI solutions for their organization. We will start with a decision tree that can guide technology choices so that you understand which services you should consider.

In this chapter, we will cover the following topics:

  • Cognitive Services/bots
  • Azure Machine Learning Studio
  • Azure Machine Learning services
  • Machine Learning Server
  • Azure Databricks