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.

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
AI Cloud Foundations
AI Cloud Foundations
The importance of artificial intelligence
The emergence of the cloud
The Microsoft cloud – Azure
Summary
2
Data Science Process
Data Science Process
TDSP stages
Tools for TDSP
Summary
3
Cognitive Services
Cognitive Services
Cognitive Services for Vision APIs
The Computer Vision API
Face API
Cognitive Services for Language APIs
Cognitive Services for Speech APIs
Cognitive Services for Knowledge APIs
Cognitive Services for Search APIs
Summary
Reference
4
Bot Framework
Bot Framework
What is a bot?
Creating a bot with Bot Service
Summary
5
Azure Machine Learning Studio
Azure Machine Learning Studio
Deploying an Azure AI Gallery template
Building an experiment
Deploying a model as a web service
Summary
6
Scalable Computing for Data Science
Scalable Computing for Data Science
Different scalable compute options in Azure
Introduction to DSVMs
DLVM
Batch AI service
ACI
AKS
Summary
7
Machine Learning Server
Machine Learning Server
What is Microsoft ML Server?
Machine learning with Python
Summary
8
HDInsight
HDInsight
R with HDInsight
Getting started with Azure HDInsight and ML services
HDInsight and data analytics with R
Enriching data for analysis
Summary
9
Machine Learning with Spark
Machine Learning with Spark
Machine learning with Azure Databricks
Getting started with Apache Spark and Azure Databricks
Using SQL in Azure Databricks
Machine Learning with HDInsight
HDInsight and Spark
Working with data in a Spark environment
Configuring the data science virtual machine
Setting up an HDInsight cluster with Spark
Summary
Further references
10
Building Deep Learning Solutions
Building Deep Learning Solutions
What is deep learning?
Overview of the Azure Notebook service
Overview of Azure Deep Learning Virtual Machine toolkits
An overview of the Microsoft Machine Learning Library for Apache Spark (MMLSpark)
Overview of TensorFlow on Azure
Summary
11
Integration with Other Azure Services
Integration with Other Azure Services
Logic Apps
Azure Functions
Azure Data Lake Analytics
Azure Data Factory
Summary
12
End-to-End Machine Learning
End-to-End Machine Learning
Using the Azure Machine Learning SDK for E2E machine learning
Summary
13
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HDInsight and data analytics with R

Firstly, we need to get our data into Azure so that HDInsight can see it. We can upload data directly to Azure Storage, or we can use functionality in SQL Server Integration Services (SSIS). SSIS has the capability of connecting to Azure Blob Storage and Azure HDInsight. It enables you to create integration service packages that transfer data between an Azure Blob Storage and the on-premise data source. Then, the Azure HDInsight process can conduct processing on the data.

In order to get the data into HDInsight using SSIS, it's necessary to install the Azure Feature Pack. The Microsoft SSIS Feature Pack for Azure provides SQL Server Integration Services with the capability to connect to many Azure services, such as Azure Blob Storage, Azure Data Lake Store, Azure SQL Data Warehouse, and Azure HDInsight. It is a separate install, and you...

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