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

Reviewing industry trends

In this book, we have covered many topics surrounding the implementation of AI in many scenarios where organizations can benefit from improving operations. Here are a few trends and developments that are likely to shape the future of these systems:

  • Increased capabilities: As AI research continues to advance, we can expect LLMs such as ChatGPT to become even more capable, with improved accuracy and the ability to handle a wider range of NLP tasks. This will likely lead to the development of new applications and use cases for these models.
  • Greater efficiency: LLMs such as ChatGPT are currently trained on vast amounts of data, require substantial computational resources, and can take significant time to process and train. As we have discussed several times throughout the book, there is always a trade-off between cost and time, and we are certainly seeing this same paradigm with ChatGPT. In the future, we can expect to see advances in AI algorithms...