Data Drift Monitoring and Its Importance in MLOps

Data drift is a crucial aspect of Machine Learning Operations (MLOps) that refers to the change or variation in input data over time. This can occur due to natural changes, evolving user patterns, or shifts in business environments. Ignoring data drift can lead to decreased model performance and unreliable predictions. To manage this, continuous monitoring is integrated into MLOps pipelines. Tools like whylogs help detect data drift by comparing the distribution of training data with that of production data. Once detected, mitigation strategies such as retraining models or adjusting thresholds can be implemented.