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)

Summary

As we have seen in this chapter, integrating Azure AI services with other non-AI services is easy and configuring these integrations can be done in a few simple steps. For codeless approach, Logic Apps and Data Factory provide tools to automate many data-related tasks. By leveraging AI services such as Cognitive Services or ML Studio Web Services, the incoming data can be enriched with insights and predictions produced by the AI services.

The trigger-based event handling system allows you to react to different kinds of events, for example when a new file is created or modified in cloud storage. The triggers can be used to launch data processing pipelines in scenarios where data moves infrequently and schedule-based data processing might introduce lags, since the system must wait for the scheduled time to lapse. With storage-based triggers, the data pipeline can be initiated...