title: Is it possible to predict in sagemaker without using s3
body: <p>I have a .pkl which I would like to put into production. I would like to do a daily query of my SQL server and do a prediction on about 1000 rows. The <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-batch.html" rel="nofollow noreferrer">documentation</a> implies I have to load the daily data into s3. Is there a way around this? It should be able to fit in memory no problem. </p>
  
  <p>The answer to " <a href="https://stackoverflow.com/questions/48319893/is-there-some-kind-of-persistent-local-storage-in-aws-sagemaker-model-training">is there some kind of persistent local storage in aws sagemaker model training?</a> " says that "<em>The notebook instance is coming with a local EBS (5GB) that you can use to copy some data into it and run the fast development iterations without copying the data every time from S3.</em>" The 5GB could be enough but I am not sure you can actually run from a notebook in this manner. If I set up a VPN could I just query using pyodbc?</p>
  
  <p>Is there sagemaker integration with AWS Lambda? That in combination with a docker container would suit my needs.</p>