Beyond Model Deployment: Catching Data Drift

Machine learning and deep learning techniques 'learn' by recognizing and generalizing patterns and statistical properties within the training data. The efficacy of these models in real-world scenarios is contingent on the assumption that the training data is an accurate representation of the production data. However, this assumption often breaks in the real world. Consumer behaviours and market trends may undergo gradual or even drastic shifts. Sensors responsible for data collection can experience a decline in sensitivity over time. Additionally, disruptions …

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