How to make prediction with sagemaker on pandas dataframe
You can send whatever payload you want when you call InvokeEndpoint and in whatever format. You can control the contract on either side (assuming your model supports it). If you are using a model that you didn't create, look to see if it supports pre/post processing which would allow you to define the contract yourself.
In addition to this, one thing we often see customers do is to do processing within the model instead of before calling SageMaker's InvokeEndpoint. A common use case is to accept the S3 path of the object you need to do predictions on when you call InvokeEndpoint. Then the model would be responsible for downloading the S3 item and transforming it and then running the inference on that data.
Depending on the InvokeEndpoint response, it can do the same and the model can upload it to S3 and just send the S3 key back as a response. This might not be what you are looking to do but it's just an additional example of the flexibility you have when using SageMaker.