Model monitoring is the ongoing process of managing and evaluating machine learning models in production to ensure their effectiveness and reliability over time. This involves tracking key performance metrics, identifying data and concept drift, and maintaining acceptable latency and throughput levels. Additionally, it includes setting up alerts for any performance degradation. Model monitoring is crucial for data scientists and machine learning engineers, as it helps them maintain the integrity of models in real-world applications, ensuring they continue to deliver accurate and reliable results.
