Your e-commerce platform relies on a machine learning (ML) model to recommend products to users, initially providing highly relevant recommendations that boost sales and customer engagement. However, as customer behavior shifts over time due to seasonal trends or changing preferences, the model's performance deteriorates, leading to outdated or irrelevant item suggestions, which negatively impacts conversion rates. This phenomenon is known as model drift, where the model's accuracy decreases because real-world data has changed since the model was trained. To address this issue, it is crucial to monitor ML models in production, track data distribution shifts, and maintain performance metrics to ensure accurate predictions and avoid costly errors or regulatory compliance issues. By implementing a proactive ML monitoring strategy, businesses can ensure their models remain accurate, reliable, and aligned with real-world requirements. Key challenges include data drift, concept drift, training-serving skew, and model staleness, which can be addressed by tracking essential metrics such as model performance, data quality, drift detection, latency, and computational metrics. Effective ML monitoring involves establishing a strong baseline for model performance, implementing real-time monitoring systems, automating retraining pipelines, maintaining comprehensive logging and auditing, and regularly evaluating and updating monitoring metrics to ensure models remain reliable and adaptable in production. To address these challenges, businesses must adopt a comprehensive ML monitoring strategy, such as the one offered by Acceldata, which provides full-stack observability, automated drift detection, and seamless model optimization to keep AI models performing at their best.