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)

Integration with Other Azure Services

In addition to using Azure AI services directly, Azure also provides options for using these services from other non-AI services. Many Azure AI services provide REST API interfaces that can be consumed from other services. AI services can thus be used as subcomponents of other apps to provide insights and predictions. Many non-AI services in Azure have built-in integration with AI services, so that AI components can often be added to apps with a few clicks.

Some AI services do not include any automation features. Recurring tasks, such as retraining ML models or running batch workloads, require integration with other services that offer these features. In the following sections, we will present various options for launching AI jobs automatically. In addition to traditional time-scheduled workloads, Azure services also provide objects called...