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)


A DLVM is a special type of DSVM that has a base image that is customized for deep learning. We can use either Windows 2016 or Ubuntu Linux as the OS. It has pre-installed frameworks, tools, and tutorials to get you started quickly with deep learning.

Provisioning a DLVM is similar to provisioning a DSVM. We need to select a GPU-based VM for a DLVM:

  1. Go to the Azure portal and search for deep learning virtual machine. We can create a DLVM in the same way as we created a DSVM.
  2. A window will pop up, asking us to provide a name and select an OS, username, password, resource group, location, and so on. The next window requires us to select a GPU-based VM. As shown in the following screenshot, the VM sizes that are not available in your region or with your current subscription will be grayed out:
A DLVM requires a GPU-based VM. It comes with all GPU drivers, deep learning frameworks...