### SageMaker JumpStart Image classification Training & Deployment
The notebook `Amazon_JumpStart_Image_Classification.ipynb` demos how to fine-tune and deploy a pre-trained image classification model using JumpStart API. It shows how to select a pre-trained image classification model from JumpStart and fine-tune it on an example dataset containing raw .jpg/.png images, while varying training hyperparameters such as learning rate, batch-size and number of epochs. AMT (Automatic Model Tuning) is used to search for the best hyperparameters. Once the training is complete, the notebook shows how to host the trained model for inference. It also shows how to host the pre-trained model as-it-is without first fine-tuning it.

The notebook `Amazon_JumpStart_Image_Classification_Benchmarking.ipynb` demos how to use JumpStart to perform large-scale benchmarking and model selection tasks given the large number of built-in image classification models provided by JumpStart. It shows how to use models available in the JumpStart API and asynchronously launch SageMaker hyperparameter tuning jobs. This enables model selection both within a model architecture via automatic model tuning (AMT) and across model architectures.