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)
Part 1: Ocean Smart – an AI Success Story
Part 2: Deploying Next-Generation Knowledge Mining Solutions with Azure Cognitive Search
Part 3: Other Cognitive Services That Will Help Your Company Optimize Operations

Estimating the costs for a proof-of-concept solution, including all services

To build relatively accurate costing models, Ocean Smart needed certain estimates to be made based on logic and some guesswork. When you're building your costing models, be mindful that it is very difficult to get exact costs initially. As Ocean Smart was able to bring the solution that was developed into a production state, they started to see how long it took to move data, how much data was being stored to maintain the model, how much compute was required, and how long it would take to execute model training and retraining. In turn, they began to build a basis to add more compute that was required to meet internal SLAs for processing time. The remainder of this section will go through the decisions they needed to make when it came to building an estimate for a Form Recognizer proof of concept solution in Azure. The prices and tiers of each of the components are built in the East US 2 region of Azure...