AI for Observability
The speaker explains his solution about adding AI for observability. Where observability includes logs, traces and matrices.
Features
It does not embed log message. most sophisticated GenAI also takes maximum 2 millions token. Logs generates it in 2 seconds. So solution need to feed right data to AI. It understands form log, which field shall be feed as initial value and then instruct to feed more data.
It creates visualization dashboard based on question
It has level 0 (manual observability) to level 4 (full observability)
It uses AWS Bedrock to solve privacy issue and compliance.
In future solution : GenAI
- will understand deployment
- will understand changes between deployments and its impact : cost, error increase or decrease.
- can go to Github repo to know changes that happen
- can fix the code
- then write test (UT) so it cannot happen again
So it makes much stable environment. It can make autonomous cluster configuration
At present, the solution has
- ability to analyze exception. Root cause analysis of exception. not 100% accurate all the time. It gives list of actions, that are taken to understand & troubleshoot problem. The solution can auto run RCA for each alert.
As we know GenAI has 3 models
1. generic questions
2. RAG
3. Agent
Yes, the solution will make openAI calls. every openAI call costs money. Now cost is reducing.
Future we may have trend of : BoY RAG
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