Traditional technology is deterministic: the same input always produces the same output. But AI models are not. They reason within a context, and if that context isn't carefully managed, the quality of the responses can deteriorate. What was once a correct answer may no longer be so, and an indicator like customer satisfaction begins to gradually decline. <\/p>\n\n\n\n
At that point, the risk arises that no one will notice it in time, when organizations measure the results in the first few months and celebrate them, but leave the model in production without follow-up. <\/p>\n\n\n\n
In this episode of our "Off the Record" vlog, we explain what AI observability is and how an AI model degrades. You'll learn that AI models are not deterministic and can degrade over time; that observability automatically measures quality, resource consumption, biases, and availability; that maintaining a model in production requires more effort than building it; and that what is observed can even change the original business case.<\/p>\n\n\n\n