Retrain the model: You can add new data to the existing training dataset and retrain the model with the combined dataset to make the model more accurate. Fine-tuning: In this approach, you can take an existing pre-trained model and fine-tune it with new data. This approach is especially useful when you have limited new data. Transfer learning: Transfer learning is similar to fine-tuning, but in this approach, you use a pre-trained model to extract relevant features from the new data and then train a new model on these features. This approach is especially useful when the new data is significantly different from the original data used to train the model. Incremental learning: Incremental learning involves updating the model with new data while retaining the knowledge gained from the previous data. This approach is useful when the model needs to adapt to new data without forgetting the previously learned information.