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Practical Guide to Azure Cognitive Services

Practical Guide to Azure Cognitive Services

By : Chris Seferlis , Christopher Nellis, Andy Roberts
4.9 (12)
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Practical Guide to Azure Cognitive Services

Practical Guide to Azure Cognitive Services

4.9 (12)
By: Chris Seferlis , Christopher Nellis, Andy Roberts

Overview of this book

Azure Cognitive Services and OpenAI are a set of pre-built artificial intelligence (AI) solution APIs that can be leveraged from existing applications, allowing customers to take advantage of Microsoft’s award-winning Vision, Speech, Text, Decision, and GPT-4 AI capabilities. With Practical Guide to Azure Cognitive Services, you’ll work through industry-specific examples of implementations to get a head-start in your production journey. You’ll begin with an overview of the categorization of Azure Cognitive Services and the benefits of embracing AI solutions for practical business applications. After that, you’ll explore the benefits of using Azure Cognitive Services to optimize efficiency and improve predictive capabilities. Then, you’ll learn how to leverage Vision capabilities for quality control, Form Recognizer to streamline supply chain nuances, language understanding to improve customer service, and Cognitive Search for next-generation knowledge-mining solutions. By the end of this book, you’ll be able to implement various Cognitive Services solutions that will help you enhance efficiency, reduce costs, and improve the customer experience at your organization. You’ll also be well equipped to automate mundane tasks by reaping the full potential of OpenAI.
Table of Contents (22 chapters)
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1
Part 1: Ocean Smart – an AI Success Story
5
Part 2: Deploying Next-Generation Knowledge Mining Solutions with Azure Cognitive Search
10
Part 3: Other Cognitive Services That Will Help Your Company Optimize Operations

Integrating the complete solution for production use

So, now that you can see what the architectural components are that need to be deployed, it is time to look at how to deploy the services for production use and how it is used. We are also going to look at the Form Recognizer code that is running within the container for processing the forms, and what the sample output looks like. From there, we will present a sample of the code you will use for sending the data to an ERP for further use of the extracted data.

For the purposes of demonstration, we deployed a simple web application using the Django framework that vendors can log in to and upload their invoices for processing. After the form is uploaded into the vendor's portal, it is run through the architecture described in the previous section of this chapter to extract the key information. In our first attempts, using the invoice layout model, the Form Recognizer service was unable to extract the key details of our invoices...

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