What if I need to train the model and use it for inferencing

 ​If you must customize a model's core weights because RAG alone isn't cutting it:

​Spin up a SageMaker JumpStart notebook or a light training instance to fine-tune an open-weights model (like Llama or Mistral) on your dataset. SageMaker gives you the granular logs, epoch tracking, and hyperparameter control you need during training.  

​Once training is complete, export the model weights to S3.

​Use Amazon Bedrock Custom Model Import to ingest your custom-trained model directly back into Bedrock.  

​This allows you to leverage SageMaker for the heavy data science lifting, but serve the final application through Bedrock’s serverless, highly scalable, unified API without having to manage 24/7 running inference endpoints on SageMaker.