Book description
Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies
Key Features
- Learn advanced concepts in Azure ML and the Cortana Intelligence Suite architecture
- Explore ML Server using SQL Server and HDInsight capabilities
- Implement various tools in Azure to build and deploy machine learning models
Book Description
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.
What you will learn
- Discover the benefits of leveraging the cloud for ML and AI
- Use Cognitive Services APIs to build intelligent bots
- Build a model using canned algorithms from Microsoft and deploy it as a web service
- Deploy virtual machines in AI development scenarios
- Apply R, Python, SQL Server, and Spark in Azure
- Build and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlow
- Implement model retraining in IoT, Streaming, and Blockchain solutions
- Explore best practices for integrating ML and AI functions with ADLA and logic apps
Who this book is for
If you are a data scientist or developer familiar with Azure ML and cognitive services and want to create smart models and make sense of data in the cloud, this book is for you. You'll also find this book useful if you want to bring powerful machine learning services into your cloud applications. Some experience with data manipulation and processing, using languages like SQL, Python, and R, will aid in understanding the concepts covered in this book
Table of contents
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- AI Cloud Foundations
- Data Science Process
- Cognitive Services
- Bot Framework
- Azure Machine Learning Studio
- Scalable Computing for Data Science
-
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
- How do Azure Data Factory and HDInsight interact?
- Running queries on Azure HDInsight with ML Services
- RevoScaleR in Azure
- How can we read data into HDInsight using ML Services?
- Reading data from files into Azure HDInsight ML Services
- Converting text and CSV files to the preferred XDF format
- Using the new XDF file in Microsoft ML Services
- XDF versus flat text files
- Reading data from SQL Server
- Installing R packages on Microsoft ML Services
- Analyzing and summarizing data in Microsoft ML Services
- Visualizing data
- 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
-
Open source deep learning frameworks
- In-depth analysis of Microsoft deep learning tools 
- Overview of Microsoft CNTK
- The architecture building blocks of CNTK
-
Developing and deploying CNTK layers in the Azure Deep Learning VM to implement a neural network
- CNTK inputs and variables declaration
- CNTK variables section
- Data readers for CNTK
- Operations in CNTK
- Layers of the Microsoft CNTK 
- CNTK layer provision helpers
- CNTK modules for losses and error handling
- Input training models in CNTK
- Instantiating the Trainer object   
- Defining the training session object
- The CNTK testing model
- Deploying CNTK tools by using Azure Containers (Docker)
- Keras as a backend for Microsoft CNTK
-
Open source deep learning frameworks
- An overview of the Microsoft Machine Learning Library for Apache Spark (MMLSpark)
- Overview of TensorFlow on Azure 
- Summary
- Integration with Other Azure Services
- End-to-End Machine Learning
- Other Books You May Enjoy
Product information
- Title: Hands-On Machine Learning with Azure
- Author(s):
- Release date: October 2018
- Publisher(s): Packt Publishing
- ISBN: 9781789131956
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