The project leverages AWS Services like AWS Sagemaker which helps to train and deploy the machine learning model along with the pretrained model image imported from AWS Sagemaker estimator and S3 for storing trained data and model.The use of lambda function helps to interact with the model as well as with the frontend application APIs request

For the training of the model 5,863 JPEG X-Ray pictures and two categories (Pneumonia/Normal).Dataset

Some notable hyperparameters configuration for training the model is image resolution is 224*224, with 2 types of image training used with image having pneumonia and not having pneumonia. 15 epochs, indicating the number of times the model will go through the entire dataset during training. The hyperparameter tuner will run up to 4 training jobs with different hyperparameter configurations to find the best-performing model.