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

Using the Anomaly Detector algorithms with your data

When looking at options for detecting anomalies using the Multivariate service, we can use the Async or Sync API. Your use case will dictate which one you choose, but typically the Async process is used for looking at a batch of data and pointing out the anomalous data points. The Sync process is typically used for real-time data monitoring for anomalies. This can be determined based on your particular need depending on the latency of the data and the number of data points. Let’s start with the Async API first.

Async API

To get the status of the detection activity with the Async API, you must request the status from the service by choosing the proper API. To do so, you must use the Request URL to display your Anomaly Detector service with models created earlier. After your model has been trained and you are ready for inferencing with another dataset, you can use the same process of sending your request through the API...