What features does Amazon Bedrock offer for model customization or fine-tuning with a user's own data?

Amazon Bedrock provides tools to customize foundation models using your own data, primarily through two approaches: continued pre-training and fine-tuning. Continued pre-training allows you to further train a base model on domain-specific data, which is useful for adapting it to specialized vocabulary or concepts (e.g., legal documents or medical terminology). Fine-tuning adjusts a pre-trained model for specific tasks, like improving response quality for customer support chatbots. Additionally, Bedrock integrates with Retrieval Augmented Generation (RAG), enabling models to pull real-time data from external sources without modifying the model itself, such as querying a product database for up-to-date inventory information.

To use these features, you start by preparing your data in formats like JSONL (JSON Lines) and upload it to Amazon S3. For fine-tuning, you define training parameters such as epochs, learning rate, and batch size through Bedrock’s API or console. For example, a developer could fine-tune a model on annotated customer service transcripts to improve intent recognition. Bedrock handles the training infrastructure, scaling resources as needed. For RAG, you connect Bedrock to data sources like Amazon OpenSearch or Aurora databases using Bedrock Agents, allowing the model to dynamically retrieve relevant information during inference. This avoids the need to retrain the model while ensuring responses stay current.

After customization, Bedrock deploys your model as a managed API endpoint, letting you integrate it into applications like any other Bedrock model. AWS handles security through IAM policies, encryption (both at rest and in transit), and VPC isolation. For instance, a healthcare app could deploy a model fine-tuned on clinical notes while ensuring HIPAA compliance via AWS’s security controls. Developers can test different model versions A/B style and monitor performance with CloudWatch metrics. While not all models in Bedrock support full fine-tuning (check provider documentation), options like RAG and prompt engineering offer flexibility for most use cases without requiring heavy customization.