Example notebooks for deployment, Real Time inference and Batch Transformation

You can find all the information related to the usage of our product here: https://github.com/VITechLab/aws-sagemaker-examples/blob/master/Diabetic-Retinopathy-Detector/

It contains example Jupyter Notebooks showing how to deploy the model, run Real Time inference, run Batch Transform job to perform the inference on the data stored in Amazon S3 bucket. It also contains input and output data samples. As well as the code for visualizing the prediction results.

Using our model for real time prediction using python is as simple as this:

predictor = sagemaker.predictor.RealTimePredictor(
    ' your endpoint name ',
    sagemaker_session=sagemaker.Session(),
    content_type="image/jpeg"
)

with open('data/sample_image.jpg', 'rb') as img:
    img_bytes = bytearray(img.read())
    result = predictor.predict(img_bytes).decode("utf-8")

Supported content types are [“image/jpeg”] Supported response types are “application/json”

You can find more details here: https://github.com/VITechLab/aws-sagemaker-examples/blob/master/Diabetic-Retinopathy-Detector/