Machine Learning in productie: van experimenteel naar werkelijk systeem

Moving machine learning from experimental phase to live systems requires far more than deploying a trained model—it demands robust infrastructure, continuous monitoring, and seamless integration with existing software. Data scientists must manage data pipelines, track model performance degradation, and maintain reliability for actual users.