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Hands-On  MLOps on Azure

You're reading from   Hands-On MLOps on Azure Automate, secure, and scale ML workflows with the Azure ML CLI, GitHub, and LLMOps

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Product type Paperback
Published in Aug 2025
Publisher Packt
ISBN-13 9781836200338
Length 276 pages
Edition 1st Edition
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Author (1):
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Banibrata De Banibrata De
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Banibrata De
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Foundations of MLOps
2. Understanding DevOps to MLOps FREE CHAPTER 3. Training and Experimentation 4. Part 2: Implementing MLOps
5. Reproducible and Reusable ML 6. Model Management (Registration and Packaging) 7. Model Deployment: Batch Scoring and Real-Time Web Services 8. Capturing and Securing Governance Data for MLOps 9. Monitoring the ML Model 10. Notification and Alerting in MLOps 11. Part 3: MLOps and Beyond
12. Automating the ML Lifecycle with ML Pipelines and GitHub Workflows 13. Using Models in Real-world Applications 14. Exploring Next-Gen MLOps 15. Other Books You May Enjoy
16. Index

Best practices for alert management

Effective alert management is crucial for maintaining a responsive and efficient MLOps environment. However, poorly configured alerts can lead to alert fatigue, overwhelming your team with notifications and potentially causing important issues to be overlooked. This section will cover best practices for setting alert thresholds and strategies to avoid alert fatigue.

Setting appropriate alert thresholds

When configuring alert thresholds, there are several best practices to consider:

  • Understand your baseline: Before setting thresholds, monitor your systems for a period to understand normal behavior. This baseline will help you distinguish between regular fluctuations and genuine issues.
  • Start conservative: Begin with wider thresholds and gradually tighten them as you gain more insights into your system’s behavior. This approach helps avoid an initial flood of false positives.
  • Use dynamic thresholds: Where possible...
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