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1
Introduction
2
Monitoring Issues
3
HighLevel Concepts
4
Conceptual Approach
5
Service Mesh Example
6
Simple Data Array
7
Grouping Mechanism
8
Normally Detection
9
Booking for Application
10
Anomaly Detection
11
Directed Application Graph
12
Anomaly Subgraph
13
Personal Pagerank Algorithm
14
Predictive Autoscaling
15
HPA and Predictive Autoscaling
16
Conclusion
17
Future plans
18
Thank you
Description:
Explore how to enhance your Kubernetes experience using service mesh and MLOps in this 29-minute conference talk from KubeCon + CloudNativeCon Europe 2022. Dive into practical cases from Sberbank's large private cloud implementation, featuring 50+ on-premise Kubernetes clusters, 500+ compute nodes, and 10+ Istio meshes. Learn about leveraging machine learning models to optimize application performance, with detailed insights into model architecture and training data preparation based on service mesh telemetry. Discover monitoring solutions, high-level concepts, and innovative approaches such as anomaly detection, directed application graphs, and predictive autoscaling. Gain valuable knowledge on implementing these techniques in your own Kubernetes environment and stay ahead of future developments in cloud-native technologies.

How to Improve Your Kubernetes Experience with Service Mesh and MLOps

CNCF [Cloud Native Computing Foundation]
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