"In order to diagnose these AI model problems, you need to actually monitor and analyze the data, the model, and the infrastructure together. It's not always a model problem or a data problem; it's a combination. Sometimes, it's simply your infrastructure."
"One of its customers, a major U.S. credit card company, saw that one of its fraud detection models was drifting. Because InsightFinder was monitoring all of the company's infrastructure, it was able to identify that the model drift was caused by an outdated cache in some server nodes."
Observability tools have evolved to prioritize controlling complexity and costs rather than tracking everything. The rise of AI agents in enterprises has introduced new workloads requiring observation. InsightFinder AI, founded by Helen Gu, utilizes machine learning to monitor and resolve IT infrastructure issues. The company addresses AI model reliability by integrating data, models, and infrastructure analysis. A case study involving a U.S. credit card company illustrates how InsightFinder identified model drift due to outdated server cache, emphasizing the need for comprehensive monitoring.
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