Book Image

Practical Guide to Azure Cognitive Services

By : Chris Seferlis, Christopher Nellis, Andy Roberts
Book Image

Practical Guide to Azure Cognitive Services

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)
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

Building the complete Custom Vision solution

In this chapter, we use Cognitive Services to train a predictive model and deploy the model to a web endpoint to classify new images as they arrive. The following diagram provides an overview of how the tail images are processed and tested against our model to determine whether they are good or bad, as well as how we can frequently update the model to handle new examples:

Figure 10.14 – Process flow of tail image classification testing against machine learning model

Figure 10.14 – Process flow of tail image classification testing against machine learning model

To support this process, we create the following:

  • The cognitive services and custom vision workspace.
  • A storage account where new images and results will be stored and a storage table will be used to communicate results to the frontend web application.
  • A function app that contains functions to do the following:
    1. Classify new images as they arrive. It is important to note that we were able to utilize the model trained for the...