Model customization is the process of providing training data to a model in order to improve its performance for specific use-cases. You can customize Amazon Bedrock foundation models in order to improve their performance and create a better customer experience. Amazon Bedrock currently provides the following customization methods.
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Distillation
Use distillation to transfer knowledge from a larger more intelligent model (known as teacher) to a smaller, faster, and cost-efficient model (known as student). Amazon Bedrock automates the distillation process by using the latest data synthesis techniques to generate diverse, high-quality responses from the teacher model, and fine-tunes the student model.
To use distillation, you select a teacher model whose accuracy you want to achieve for your use case, and a student model to fine-tune. Then, you provide use case-specific prompts as input data. Amazon Bedrock generates responses from the teacher model for the given prompts, and then uses the responses to fine-tune the student model. You can optionally provide labeled input data as prompt-response pairs.
For more information about using distillation see Customize a model with distillation in Amazon Bedrock.
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Fine-tuning
Provide labeled data in order to train a model to improve performance on specific tasks. By providing a training dataset of labeled examples, the model learns to associate what types of outputs should be generated for certain types of inputs. The model parameters are adjusted in the process and the model's performance is improved for the tasks represented by the training dataset.
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Continued pre-training
Provide unlabeled data to pre-train a foundation model by familiarizing it with certain types of inputs. You can provide data from specific topics in order to expose a model to those areas. The Continued Pre-training process will tweak the model parameters to accommodate the input data and improve its domain knowledge.
For example, you can train a model with private data, such as business documents, that are not publicly available for training large language models. Additionally, you can continue to improve the model by retraining the model with more unlabeled data as it becomes available.
For information about model customization quotas, see Amazon Bedrock endpoints and quotas in the AWS General Reference.
Note
You are charged for model training based on the number of
tokens processed by the model (number of tokens in training data corpus × number of
epochs) and model storage charged per month per model. For more information, see Amazon Bedrock pricing
Guidelines for model customization
The ideal parameters for customizing a model depend on the dataset and the task for which the model is intended. You should experiment with values to determine which parameters work best for your specific case. To help, evaluate your model by running a model evaluation job. For more information, see Evaluate the performance of Amazon Bedrock resources.
Use the training and validation metrics from the output files generated when you submit a model customization job to help you adjust your parameters. Find these files in the Amazon S3 bucket to which you wrote the output, or use the GetCustomModel operation.
