Exploring ChatGPT for Improved Observability
summary
Modern observability must handle hyperscaler fragility: 2022 Google maintenance causes 3h22m latency, March Traffic Director outage 2h35m, Azure East US2 ~12h and AWS us‑east power failure in July, driving high downtime costs (average $5,600/min in 2014, industry estimates up to ~$260,000/hour). Proposes using GPT-3.5 (175B params) for conversational UI, code generation, alert enrichment and intelligent problem remediation while warning of non‑determinism, prompt engineering needs, prompt‑injection risks, $20M+ training energy costs, and hyperscaler pushes (Azure $10B OpenAI, Google Gemini/Palm2, AWS CodeWhisperer).
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chapters (21)
- 0:00intro
- 0:21preface
- 0:31why do we care about observability?
- 0:51modern platforms are ephemeral
- 3:44cost of unexpected downtime has risen significantly
- 4:29todays observability challenges
- 5:21risk of it outages are set to grow even further in 2023
- 5:56modern observability solutions
- 6:53there is currently a lot of innovation occuring in ai, in particular llm's
- 8:02imitate brain-like functionality using deep neural networks
- 9:25dnn models are pre-trained from "the whole internet"
- 10:51chat gpt
- 11:52chat gpt is built on top of latest breakthroughs in language model design
- 13:05chat gpt ≠ skynet
- 14:58use cases of observability
- 15:56keep in mid that chat gpt's responses are non-deterministic
- 17:03chat gpt's informed decision making
- 18:08prompt engineering is a new discipline
- 20:48hyperscalers are investing heavily into gpt's
- 22:02so are large models a panacea?
- 22:55dynatrace - cloud done right
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