Book Image

Learning Microsoft Cognitive Services - Third Edition

By : Leif Larsen
Book Image

Learning Microsoft Cognitive Services - Third Edition

By: Leif Larsen

Overview of this book

Microsoft Cognitive Services is a set of APIs for integrating artificial intelligence in your applications to solve logical business problems. If you’re new to developing applications with AI, Learning Microsoft Cognitive Services will give you a comprehensive introduction to Microsoft’s AI stack and get you up-to-speed in no time. The book introduces you to 24 APIs, including Emotion, Language, Vision, Speech, Knowledge, and Search. Using Visual Studio, you can develop applications with enhanced capabilities for image processing, speech recognition, text processing, and much more. Moving forward, you will work with datasets that enable your applications to process various data in the form of image, video, or text. By the end of the book, you’ll be able to confidently explore Cognitive Services APIs for building intelligent applications that can be deployed for real-world business uses.

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Table of Contents (17 chapters)
Learning Microsoft Cognitive Services - Third Edition
Learning Microsoft Cognitive Services - Third Edition
Contributors
Contributors
Acknowledgments
Acknowledgments
Preface
Preface
Free Chapter
1
Getting Started with Microsoft Cognitive Services
Getting Started with Microsoft Cognitive Services
Cognitive Services in action for fun and life-changing purposes
Setting up the boilerplate code
Detecting faces with the Face API
An overview of different APIs
Getting feedback on detected faces
Summary
2
Analyzing Images to Recognize a Face
Analyzing Images to Recognize a Face
Analyze an image using the Computer Vision API
Diving deep into the Face API
Adding identification to our smart-house application
Knowing your mood using the Face API
Automatically moderating user content
Building your own image classifiers
Summary
3
Analyzing Videos
Analyzing Videos
Diving into Video Indexer
Unlocking video insights using Video Indexer
Summary
4
Letting Applications Understand Commands
Letting Applications Understand Commands
Creating language-understanding models
Training a model
Summary
5
Speaking with Your Application
Speaking with Your Application
Converting text to audio and vice versa
Knowing who is speaking
Verifying a person through speech
Customizing speech recognition
Translating speech on the fly
Summary
6
Understanding Text
Understanding Text
Setting up a common core
Correcting spelling errors
Extracting information through textual analysis
Translating text on the fly
Summary
7
Building Recommendation Systems for Businesses
Building Recommendation Systems for Businesses
Providing personalized recommendations
Summary
8
Querying Structured Data in a Natural Way
Querying Structured Data in a Natural Way
Tapping into academic content using the academic API
Interpreting natural language queries
Finding academic entities in query expressions
Calculating the distribution of attributes from academic entities
Entity attributes
Creating the backend using the Knowledge Exploration Service
Defining attributes
Adding data
Building the index
Understanding natural language
Local hosting and testing
Going for scale
Answering FAQs using QnA Maker
Creating a knowledge base from frequently asked questions
Training the model
Publishing the model
Summary
9
Adding Specialized Searches
Adding Specialized Searches
Searching the web using the smart-house application
Getting the news
Searching for images and videos
Helping the user with autosuggestions
Search commonalities
Searching for visual content using Bing Visual Search
Adding a custom search
Summary
10
Connecting the Pieces
Connecting the Pieces
Completing our smart-house application
Real-life applications using Microsoft Cognitive Services
Where to go from here
Summary
LUIS Entities
LUIS Entities
LUIS prebuilt entities
License Information
License Information
Video Frame Analyzer
OpenCvSharp3
Newtonsoft.Json
NAudio
Index
Index

Going for scale

While it is nice to be able to create local prototypes, the limitations ensure that we need to deploy the service elsewhere for production. In this case, this means deploying the KES to Microsoft Azure.

We will now look at the steps required to deploy the KES to Microsoft Azure.

Hooking into Microsoft Azure

The first step is to download the Azure publish settings file. This needs to be saved as AzurePublishSettings.xml and stored in the directory in which kes.exe runs.

Note

You can find the Azure publish settings file at https://manage.windowsazure.com/publishsettings/.

There are two ways to build and host the KES without restrictions. The first way is to boot up a Windows virtual machine in Azure. On this VM, you should follow the same steps that we took locally. This allows for rapid prototyping, but without any restrictions.

The second way is to run kes.exe locally, but adding --remote as a parameter. This will create a temporary Azure VM, build the index, and upload the index...

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