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Modern Network Observability

You're reading from   Modern Network Observability A hands-on approach using open source tools such as Telegraf, Prometheus, and Grafana

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781835081068
Length 506 pages
Edition 1st Edition
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Authors (3):
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Christian Adell Christian Adell
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Christian Adell
David Flores David Flores
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David Flores
Josh VanDeraa Josh VanDeraa
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Josh VanDeraa
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Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1:Understanding Monitoring and Observability
2. Chapter 1: Introduction to Monitoring and Observability FREE CHAPTER 3. Chapter 2: Role of Monitoring and Observability in Network Infrastructure 4. Chapter 3: Data’s Role in Network Observability 5. Part 2: Building an Effective Observability Stack
6. Chapter 4: Observability Stack Architecture 7. Chapter 5: Data Collectors 8. Chapter 6: Data Distribution and Processing 9. Chapter 7: Data Storage Solutions for Network Observability 10. Chapter 8: Visualization – Bringing Network Observability to Life 11. Chapter 9: Alerting – Network Monitoring and Incident Management 12. Chapter 10: Real-World Observability Architectures 13. Part 3: Using Your Network Observability Data
14. Chapter 11: Applications of Your Observability Data – Driving Business Success 15. Chapter 12: Automation Powered by Observability Data – Streamlining Network Operations 16. Chapter 13: Leveraging Artificial Intelligence for Enhanced Network Observability 17. Index 18. Other Books You May Enjoy Appendix A

AI and ML fundamentals

Like any new technology, there’s still a bit of confusion around the scope and meaning of each term. Before diving deep into the challenges that AI/ML can help solve, it’s important to demystify some classification terms:

  • AI is the field of knowledge that tries to make machines reproduce human behavior. Any software that imitates this behavior can be called AI (for example, a simple if this, then do that rule).
  • ML adds the capability to learn/infer patterns from historical data so that it can be applied to new data. It produces new outputs by reusing the learned knowledge.
  • Neural networks are a subset of ML that emulate how the human brain works while leveraging the concept of neurons and how they’re connected. This allows more complex problems to be solved.
  • Deep learning is a multilayer neural network (more than three layers) that provides more options for building custom neural networks, increasing the capacity to tune...
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