The overall architecture of a real-time ML use case requires 3 mandatory pieces: I would recommend pushing or syncing data rather than pulling with a cloud service.
The Notebook can be used for data manipulation, algorithm selection, data analysis, creating and kicking off training jobs, and even deploying your newly trained model into production!
The project involved setting up a data science environment, which led me to AWS SageMaker If you’re just interested in the solution architecture, skip ahead AWS Sagemaker is a fully-managed service providing development, training and hosting capabilities.
Prior to my exposure to public cloud services, I spent a lot of time working in hadoop distributions to deliver the processing power and storage requirements for data lake construction, and utilized Docker to provide data science sandboxes running R studio or Jupyter notebook.
A SageMaker endpoint provides access for other AWS services to call the model and receive inferences.