Accelerate custom LLM deployment: Fine-tune with Oumi and deploy to Amazon Bedrock

This article demonstrates how to fine-tune open source LLMs using Oumi on Amazon EC2, store artifacts in S3, and deploy to Amazon Bedrock for managed inference.

• Oumi streamlines LLM lifecycle with recipe-driven training and flexible fine-tuning methods like LoRA
• Fine-tune Llama-3.2-1B-Instruct on GPU-optimized EC2 instances with distributed training support
• Store versioned model checkpoints and metadata in S3 for reproducibility and durability
• Deploy via Amazon Bedrock Custom Model Import for automatic managed inference scaling
• Integrated evaluation using benchmarks and LLM-as-a-judge without additional tooling
• Optional synthetic data generation using Amazon Bedrock as inference backend
• Companion GitHub repository provides scripts, IAM policies, and configuration templates
• Addresses challenges: iteration speed, reproducibility, scalable inference, security, cost optimization

This workflow eliminates friction between experimentation and production by combining Oumi's modular training framework with Amazon Bedrock's managed inference, enabling rapid deployment of custom LLMs.