Hands-On Machine Learning with Azure

By : Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak, Murphy

2.7 (3)

Buy this Book

Hands-On Machine Learning with Azure

2.7 (3)

By: Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak, Murphy

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

AI Cloud Foundations

The importance of artificial intelligence

The emergence of the cloud

The Microsoft cloud – Azure

Summary

Data Science Process

TDSP stages

Tools for TDSP

Summary

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

Bot Framework

What is a bot?

Creating a bot with Bot Service

Summary

Azure Machine Learning Studio

Deploying an Azure AI Gallery template

Building an experiment

Deploying a model as a web service

Summary

Scalable Computing for Data Science

Different scalable compute options in Azure

Introduction to DSVMs

DLVM

Batch AI service

ACI

AKS

Summary

Machine Learning Server

What is Microsoft ML Server?

Machine learning with Python

Summary

HDInsight

R with HDInsight

Getting started with Azure HDInsight and ML services

HDInsight and data analytics with R

Enriching data for analysis

Summary

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

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

Integration with Other Azure Services

Logic Apps

Azure Functions

Azure Data Lake Analytics

Azure Data Factory

Summary

End-to-End Machine Learning

Using the Azure Machine Learning SDK for E2E machine learning

Summary

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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...