Can I fine-tune all models available in Bedrock or only certain ones? How do I select which model to fine-tune?

AWS Bedrock provides access to a variety of foundation models, but not all of them support fine-tuning. The availability of fine-tuning depends on the model provider and their specific policies. For example, some models like Anthropic’s Claude or AI21 Labs’ Jurassic-2 family may offer fine-tuning options, while others might restrict customization to prompt engineering or retrieval-augmented generation (RAG). Before starting, you should check the Bedrock documentation or the model provider’s page to confirm whether a specific model supports fine-tuning. If a model does support it, Bedrock typically provides APIs or workflows to upload training data, configure hyperparameters, and deploy the tuned model. However, models from providers like Meta’s Llama or Stability AI’s Stable Diffusion might not support fine-tuning in Bedrock at all, limiting you to inference-only use cases.

To select a model for fine-tuning, start by identifying your task requirements. For instance, if you’re building a text summarization tool, a model like Claude might be suitable due to its strong performance in language tasks. If your application requires multilingual support, Amazon Titan could be a better fit. Next, evaluate the model’s base capabilities using Bedrock’s playground or sample prompts to ensure it aligns with your use case before investing time in fine-tuning. Also, consider cost and scalability: larger models may yield better results but could increase training and inference expenses. Check the provider’s documentation for details on data formatting, training time, and hardware requirements. For example, fine-tuning Claude might require your dataset to be in JSONL format with specific prompt-completion pairs, while Jurassic-2 could have different guidelines. Finally, verify that the model’s license and usage terms allow commercial applications if that’s part of your project.

To implement fine-tuning in Bedrock, navigate to the model’s details in the AWS console. If fine-tuning is supported, you’ll see options to create a training job, upload data, and specify parameters like epochs or learning rate. Use AWS CloudFormation or the Bedrock API to automate this process if you’re integrating it into a pipeline. After training, validate the tuned model using a separate test dataset and compare metrics like accuracy or latency against the base model. For example, if you fine-tuned Claude for a customer support chatbot, measure response relevance before and after tuning. Keep in mind that fine-tuning is irreversible—you can’t revert to the original model once it’s customized. If you’re unsure which model to choose, start with smaller-scale experiments and iterate based on performance and cost trade-offs.