Generative AI and Observability Automation

Sajid Mehmood, Datadog
Michael Gerstenhaber, Datadog
One of the biggest challenges in observability is separating the signal from the noise. As artificial intelligence (AI) tools become more powerful and accessible, it has generated a lot of buzz around the role of AI with respect to the performance and reliability of our technical systems and the teams that build and operate them. In this fireside chat, Michael Gertenhaber (Datadog VP of Product) and Sajid Mehmood (Datadog VP of Engineering) will sift through the hype to chat about what generative AI and Large Language Models (LLMs) will really mean for the future of observability and how it can benefit your teams today.

[@LucasCastlebane]

Generative ai content is my salvation.

[@Wise4HarvestTime]

A latency sensitive place... Meaning... Trace backs are hard to achieve? Because it takes time to get there. I like the precise query being accessible. Save the route to the answer and express it precisely... The eval of the quality of the answer and the context window is quite a black art

[@ArroyoGuitars]

Exactly...this is where the human element comes into play initially to train

[@LucasCastlebane]

👏👏👏

[@Wise4HarvestTime]

Sup data Dawgs? Not to be confused with date a dog. It's nice to hear about observability

[@LucasCastlebane]

Watchdog legion ai generative super quantum roko basilisk ultra data