Updating or retraining a custom model on AWS Bedrock when new training data becomes available involves a structured workflow to ensure improvements are effectively integrated. First, you’ll need to prepare your new data and combine it with existing training datasets. Bedrock allows you to upload additional data to your existing dataset storage (like Amazon S3) and reference it when creating a new training job. For example, if your model was initially trained on customer support tickets from 2022, you might add 2023 data to capture newer language patterns. Bedrock’s training process typically requires specifying the updated dataset location and re-running the fine-tuning job using the same base model, but with the expanded dataset. This ensures the model learns from both historical and new examples.
Next, you’ll configure and execute the retraining job through Bedrock’s API or console. When setting up the job, you can adjust hyperparameters like learning rate or batch size to optimize for the updated dataset size or complexity. For instance, if your new data includes technical jargon from a recent product launch, you might increase the number of training epochs to help the model grasp niche terminology. After initiating the job, Bedrock manages the infrastructure, and you can monitor progress via CloudWatch metrics. Once complete, evaluate the updated model’s performance against a validation set that includes examples from both old and new data to ensure it hasn’t forgotten prior knowledge (a common issue called “catastrophic forgetting”). Tools like Bedrock’s built-in evaluation workflows or custom scripts can automate this comparison.
Finally, deploy the improved model version and validate it in a staging environment before replacing the production endpoint. For example, if your model powers a chatbot, test it with real user queries that reflect both older and newer use cases. Bedrock supports versioning, allowing you to roll back if the update introduces regressions. To maintain continuous improvement, consider automating data collection and retraining triggers—like retraining monthly or when accuracy drops below a threshold. However, balance frequency with cost: each training job incurs compute expenses, and over-retraining can lead to diminishing returns. By systematically integrating new data, tuning parameters, and validating changes, you can iteratively enhance your Bedrock model’s performance while maintaining reliability.