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[DSC Europe 22] AIOPS – How can machine learning help in IT operations - Damir Kopljar
- 1. Meet Filip COMPANY: Big Telco ROLE : Director of Operations & E2E QA
- 2. THE PROBLEM • Telco mobile app is not working • >300 IT systems, 150+ mil logs daily • How to write a good rule?
- 3. Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Today’s systems are getting harder and harder to monitor Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 ms 00 2 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Latency: 50ms Requests: 200 40x Errors: 2 50x Errors: 1 Memory: 150mb Disk: 20% Spans: 20 Num of users: 200
- 4. Problem definition Train normal behavior based on historic data Idea: Let machine learning model learn what is normal state Detect anomalies in real time
- 5. How to train the model? Avg latency Raw logs 30 sec features Normal state for service X Num of logs Errors Part of day New data Machine learning model
- 6. Problem started System doesn’t look good System seems normal Service 1 getDetails endpoint 42 errors High latency
- 7. Building AIOPS solution App logs APM logs DB logs ETL service Feature store AI service Anomaly score Alert Action Model repository Dataset repository Retrain jobs Training environment Data prep jobs Inferencing environment
- 8. Traditional programming Data Rules Machine learning Data Desired behavior Desired behavior Rules Some problems require paradigm shift
- 9. Reusable Building Blocks
- 10. How can machine learning help you in your business context?
