Continuous Training for Machine Learning – a Framework for a Successful Strategy - KDnuggets

ML models are built on the assumption that the data used in production will be similar to the data observed in the past, the one that we trained our models on. While this may be true for some specific use cases, most models work in dynamic data environments where data is constantly changing and where "concept drifts" are likely to happen and adversely impact the models' accuracy and reliability. To deal with this, ML models need to be retrained regularly. Or, as stated in Google's "MLOps: Continuous delivery and automation pipelines in machine learning": "To address these challenges and to maintain your model's accuracy in production, you need to do the following: Actively monitor the quality of your model in production [...] and frequently retrain your production models." This concept is called'Continuous Training' (CT) and is part of the MLOps practice. Continuous training seeks to automatically and continuously retrain the model to adapt to changes that might occur in the data.