Modellovervåkning

Modellovervåkning, also known as model monitoring or model drift detection, is a critical aspect of machine learning operations. It involves the continuous assessment of machine learning models in production to ensure they maintain their performance and accuracy over time. As models are deployed and used in real-world applications, they may experience changes in the data they process, leading to a phenomenon known as model drift. This drift can degrade the model's performance, making it less reliable and accurate.

There are several types of model drift, including concept drift, where the statistical properties of the target variable change over time, and data drift, where the input data distribution changes. Monitoring these drifts is essential for maintaining the integrity and effectiveness of machine learning systems.

Techniques for modellovervåkning include statistical methods, such as hypothesis testing and drift detection algorithms, as well as more advanced methods like concept drift detection algorithms and anomaly detection techniques. These methods help identify when a model's performance has degraded and when it needs to be retrained or updated.

Effective modellovervåkning requires a combination of automated tools and human oversight. Automated tools can continuously monitor model performance and detect anomalies, while human experts can provide insights and make decisions based on the detected issues. This collaborative approach ensures that models remain reliable and effective in their intended applications.