Observability provides insight into system health and performance, addressing the common lack of a methodical process for resolving issues in complex environments.
A real-world example of a distributed trace highlights the challenges of debugging systems with thousands of microservices, databases, and daily deployments.
Effective problem-solving requires leveraging the distinct strengths of metrics, events, logs, and distributed traces to gain a complete picture of system behavior.
A comprehensive observability strategy involves monitoring all application layers and utilizing platform features like workloads, change tracking, and AI-driven intelligence.
Insights from a Microsoft Azure study reveal that most production issues stem from software faults or bad data, making rollbacks a common and effective first solution.
Follow a structured process for incident resolution by first checking for changes and errors, then examining local and remote dependencies before using traces to investigate further.
Combat AI hallucinations by constraining model inputs and outputs, providing additional context through retrieval-augmented generation (RAG), and eventually fine-tuning the model.
Evaluate the trade-offs between using consumption-based AI tools and building smaller, custom LLMs based on factors like request volume, cost, and data privacy.
35:16Ankit Patel
28:09Jan Oberhauser, Mirko Novakovic, Alex Laubscher & Keno Dreßel
23:55Zan Markan
41:06Lars Gentsch
24:00Nele Uhlemann
30:49Marin Niehues
26:47Alejandro Saucedo
30:36Mackenzie Jackson
Jobs that call for the skills explored in this talk.
