Fine-tune and deploy language models with Amazon SageMaker Canvas and Amazon Bedrock

This article demonstrates how to fine-tune and deploy large language models (LLMs) with Amazon Bedrock and Amazon SageMaker Canvas. It explains the process step-by-step, from preparing a dataset to creating a custom model, fine-tuning it, analyzing its performance, testing, and deploying it as an API endpoint.

Specifically, the article covers:

• Solution overview and prerequisites
• How to prepare a dataset for fine-tuning
• Creating a new model and importing the dataset in SageMaker Canvas
• Selecting a foundation model like Amazon Titan Text G1-Express LLM
• Analyzing the fine-tuned model's performance metrics
• Testing the model in the Canvas playground
• Deploying the model as an API endpoint with Amazon Bedrock
• Using the deployed model in application code
• Conclusion highlighting the potential applications