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

Summary

Here, and in the previous few chapters, we have helped to provide you with a broad overview and real solutions to apply the capabilities by combining Azure Cognitive Services technologies. Many organizations struggle with leveraging the significant volumes of data that lie in storage and file shares untapped for the significant value they hold. Beyond simply returning search results and bringing the data to the surface, we can also begin to analyze the data deeper and begin mining the data for other potential uses of AI by building machine learning models to predict future results.

A fully built KM solution is no small undertaking, but the value can greatly outweigh the costs of deployment and services. A small proof of concept to surface some of the details of latent data could be a great place to start, and relatively cost-effective. After making a case for a full implementation, you can harness all the power of the full solution we laid out in these chapters. Whether...