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Learning Microsoft Cognitive Services

Learning Microsoft Cognitive Services

By : Leif Larsen Henning Larsen
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Learning Microsoft Cognitive Services

Learning Microsoft Cognitive Services

By: Leif Larsen Henning 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.
Table of Contents (14 chapters)
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13
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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