Description: Model retraining is the process of updating a machine learning model with new data to improve its performance. This process is fundamental in the MLOps field, where the goal is to maintain the relevance and accuracy of models over time. As more data is collected, the original model, trained on a more limited dataset, may become outdated or less effective. Retraining allows the model to adapt to changes in data patterns, which is especially important in applications where conditions can vary rapidly, such as in trend prediction or recommendation systems. This process not only involves incorporating new data but also continuously evaluating the model’s performance, ensuring it remains aligned with business objectives and user expectations. Additionally, retraining may include adjustments to hyperparameters and model architecture, allowing for optimization of performance based on the characteristics of the new data. In summary, model retraining is an essential practice in MLOps that ensures machine learning models remain effective and useful over time.
