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YouTube

Scalable Anomaly Detection - With Zero Machine Learning

Strange Loop Conference via YouTube

Overview

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This course teaches learners how to build a scalable anomaly detection system without using machine learning. The course covers topics such as stream processing, anomaly detection algorithms, and a rules engine. The teaching method includes discussing the system architecture and deep-diving into the anomaly detection algorithm. The intended audience for this course is individuals interested in anomaly detection in large-scale distributed systems.

Syllabus

Introduction
Netflixs Microservices
Time Series Database
Alerts Only for Zul
Dynamic Thresholds
Adaptive Thresholds
Anomaly Detector Raju
Results
No Machine Learning
How We Built It
Impact Graph
Context
Accuracy
Operational Burden
Realtime alerting
Realtime events
Mantis
How it works
Querying
Stream Processing
Aggregate
Job Chain
Requirements
Median estimation
Mad
Raju
Simple
Recovery Detection
Recovery Algorithm
What Raju Looks Like
Permutations
Data Visualization
Impact Assessment
Timeline of Events
What is API
Another needle in a haystack
Example
Gold Standard KPI
Spinnaker Events
Emailing the culprits
Benefits
Conclusion

Taught by

Strange Loop Conference

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