Speaker
Description
Modern digital services generate enormous volumes of log data every second. When something goes wrong, performance drops, or users start reporting failures, engineers turn to logs to understand what happened. In theory, logs contain all the answers. In practice, finding them is slow, manual, and stressful. Teams must sift through millions of technical messages under time pressure, trying to translate cryptic values into meaningful information. This process often becomes the bottleneck of incident response. This talk presents an alternative approach that focuses on meaning rather than patterns alone. Instead of treating dynamic values as anonymous parameters, modern AI language techniques analyze each value together with its surrounding text. By understanding context, the system can infer what the value represents and automatically match it to known data fields described in natural language and supported by examples.
What will the TNC audience take away from your talk?
Attendees will learn how semantic AI can eliminate manual parameter interpretation in logs and automatically map raw messages to meaningful data fields. The talk shows how this approach can be integrated into existing log management environments to accelerate troubleshooting and reduce operational effort.
| Are you a first time speaker at TNC? | Yes |
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