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Hey @Bo! ML Observability is the practice of obtaining a deep understanding into your model’s data and performance across its lifecycle. Observability doesn’t just stop at surfacing a red or green light, but enables ML practitioners to root cause/explain why a model is behaving a certain way in order to improve it. Teams need it because model issues happen all the time in the real world! Just like we monitor software applications, ml models need to be monitored and when things inevitably go wrong, teams need tools to troubleshoot them!

Simple question, but is there a difference between observability and monitoring?

@Ashley Zhao great question. Can you post this in the main channel? I think this question is very good and should get more visibility