What is AI Observability?
It is the process of tracking how artificial intelligence systems make decisions and where they make mistakes by monitoring their inner world.
Overview
Artificial intelligence can operate like a 'black box'; So it can be difficult to understand why you gave an answer. Observability records every step of the system, allowing you to see the performance, cost and logical errors of the model. When an error occurs, 'why did it happen?' It makes it easier for you to find answers to your questions.
How it works
The model's inputs and outputs are monitored, processing times are measured, and the accuracy of the results is constantly checked. This data is reported in special panels.
Where it is used
It is used in production artificial intelligence applications and critical decision support systems.
Commonly confused with
It is confused with monitoring; monitoring is just 'is it working?' observability asks 'why does it work like this?' focuses on the question.
Frequently asked questions
Why is this needed?
It is necessary to catch unexpected errors of artificial intelligence and improve the system.
Is it a difficult process?
Yes, because recording every decision of the AI can create a huge data load.
Related terms
Related tools
This explanation was written in plain language for TreScout and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →