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Practical Guide to Azure Cognitive Services

You're reading from   Practical Guide to Azure Cognitive Services Leverage the power of Azure OpenAI to optimize operations, reduce costs, and deliver cutting-edge AI solutions

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781801812917
Length 454 pages
Edition 1st Edition
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Authors (3):
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Andy Roberts Andy Roberts
Author Profile Icon Andy Roberts
Andy Roberts
Chris Seferlis Chris Seferlis
Author Profile Icon Chris Seferlis
Chris Seferlis
Christopher Nellis Christopher Nellis
Author Profile Icon Christopher Nellis
Christopher Nellis
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Table of Contents (22) Chapters Close

Preface 1. Part 1: Ocean Smart – an AI Success Story
2. Chapter 1: How Azure AI Changed Ocean Smart FREE CHAPTER 3. Chapter 2: Why Azure Cognitive Services? 4. Chapter 3: Architectural and Cost Optimization Considerations 5. Part 2: Deploying Next-Generation Knowledge Mining Solutions with Azure Cognitive Search
6. Chapter 4: Deriving Value from Knowledge Mining Solutions in Azure 7. Chapter 5: Azure Cognitive Search Overview and Implementation 8. Chapter 6: Exploring Further Azure Cognitive Services for Successful KM Solutions 9. Chapter 7: Pulling It All Together for a Complete KM Solution 10. Part 3: Other Cognitive Services That Will Help Your Company Optimize Operations
11. Chapter 8: Decluttering Paperwork with Form Recognizer 12. Chapter 9: Identifying Problems with Anomaly Detector 13. Chapter 10: Streamlining the Quality Control Process with Custom Vision 14. Chapter 11: Deploying a Content Moderator 15. Chapter 12: Using Personalizer to Cater to Your Audience 16. Chapter 13: Improving Customer Experience with Speech to Text 17. Chapter 14: Using Language Services in Chat Bots and Beyond 18. Chapter 15: Surveying Our Progress 19. Chapter 16: Appendix – Azure OpenAI Overview 20. Index 21. Other Books You May Enjoy

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...

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