Example Jupyter notebooks that demonstrate how to use Amazon SageMaker and the Amazon SageMaker Python SDK.
| Notebook | Type | Description |
|---|---|---|
| 01 Extending AWS DLC image | Containers | Learn how to extend the functionality of the AWS Deep Learning Container (DLC) image to meet your specific requirements. |
| 02 Deploy Stable Diffusion2.1 with Custom image and custom script | Inference | End-to-end example of deploying Stable Diffusion2.1 by utilizing a custom image and custom script tailored to your needs. |
| 03 Deploy Stable Diffusion2.1 with DJL serving | Inference | End-to-end example of deploying Stable Diffusion2.1 using DJL serving, enabling efficient serving of your model for inference. |
| 04 Deploy Stable Diffusion2.1 with Inferentia2 | Inference | End-to-end example on how to deploy Stable Diffusion2.1 using Inferentia2, taking advantage of AWS's machine learning accelerator. |
| 05 How to create an AI image generator application using vectorize database | POC | Develop an AI application capable of generating images using a vectorized database, enabling flexible and dynamic image synthesis. |
| 06 Sagemaker inference types | Inference | Explore AWS SageMaker Inference Types for optimized model deployment. |
| 07 AWS Bedrock-powered Confluence QA App with Streamlit | POC | Discover AWS Bedrock-Powered Confluence QA App and its Potential |
| 08 TextGen Workshop | Workshop | Dive into TextGen's capabilities in this workshop and learn how to leverage its features for text generation tasks. |