Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 134 discussion

B. Continuous pre-training

This strategy involves regularly updating the foundation model with new data, ensuring the model stays relevant and accurate over time. It allows for the continuous improvement of the model as new information becomes available.

Answer: B. Continuous pre-training To keep a foundation model (FM) updated with the most recent data on a regular basis, you need a training approach that continually integrates new information. Continuous pre-training fits this requirement because it periodically (or even continuously) retrains or fine-tunes the model with the latest data, ensuring relevance and improved performance. Here's why the other options are less suitable: A. Batch learning: Trains in large, discrete batches and may introduce significant delays between training cycles, potentially causing the model to become stale. C. Static training: Trains the model once and does not update it with new data, leading to outdated predictions. D. Latent training: Not a standard industry term or recognized strategy for regularly updating foundation models.

Continuous pre-training involves regularly updating the foundation model (FM) with new data, ensuring the model stays relevant by incorporating the latest information.

To keep a foundation model (FM) relevant by using the most recent data and implementing a model training strategy that includes regular updates, the best solution is Continuous pre-training. This approach involves continuously updating the model with new data, ensuring that it remains current and effective