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Summary
The complete digitization of the world in the last few years has created both a unique opportunity and challenge for organizations. But with an increased reliance on ML models comes an even greater need to monitor model performance and build trust into AI. Fluctuations can happen because ML models are trained on historical data that might look different from the real data they see in production. Models can amplify hidden bias in the data they are trained on, putting the business in legal and reputational risk, and potentially leading to harmful results for consumers. ML Ops teams are also more likely to have low confidence in their models, which can result in more time spent on a project and more mistakes. They also come in many different forms and modalities tabular, time series, text, image, video, and audio. The technology automatically reassesses model business value and performance on an ongoing basis, issuing alerts on model performance in production and helping developers respond proactively at the first sign of deviation.
Show Notes
Solving for ML Model Monitoring Challenges with Model Performance Management (MPM)The complete digitization of the world in the last few years has created both a unique opportunity and challenge for organizations.
But with an increased reliance on ML models comes an even greater need to monitor model performance and build trust into AI.
The Challenges with Model Monitoring TodayUnfortunately, model monitoring has become more complex as a result of the vast variety and amount of ML models organizations today rely on.
Some companies have deployed traditional infrastructure monitoring solutions designed to support broad operational visibility to overcome these challenges.
ML models require continuous model monitoring and retraining throughout their entire lifecycle.
Source
https://www.enterpriseai.news/2022/10/04/solving-for-ml-model-monitoring-challenges-with-mpm/
