To keep a foundation model (FM) relevant with the most recent data, the company needs a training strategy that supports regular updates. Continuous pre-training involves periodically updating a pre-trained model with new data to improve its performance and relevance over time, making it the best fit for this requirement.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Continuous pre-training is a strategy where a pre-trained model is periodically updated with new data to keep it relevant and improve its performance. This approach is commonly used for foundation models to ensure they adapt to new trends and information."
(Source: AWS AI Practitioner Learning Path, Module on Model Training Strategies)
Detailed Explanation:
Option A: Batch learningBatch learning involves training a model on a fixed dataset in batches, but it does not inherently support regular updates with new data to keep the model relevant over time.
Option B: Continuous pre-trainingThis is the correct answer. Continuous pre-training updates the FM with recent data, ensuring it stays relevant by adapting to new trends and information.
Option C: Static trainingStatic training implies training a model once on a fixed dataset without updates, which does not meet the requirement for regular updates.
Option D: Latent trainingLatent training is not a standard term in AWS or ML contexts. It may refer to latent space in models like VAEs, but it is not a strategy for regular model updates.
[References:, AWS AI Practitioner Learning Path: Module on Model Training Strategies, Amazon Bedrock User Guide: Model Customization and Updates (https://docs.aws.amazon.com/bedrock/latest/userguide/custom-models.html), AWS Documentation: Machine Learning Training Strategies (https://aws.amazon.com/machine-learning/), , , ]
