The requirement is that I have to trigger a SageMaker endpoint on lambda to get predictions(which is easy) but have to do some extra processing for variable importance using packages such as XGBoost and SHAP.
I am able to hit the endpoint and get variable importance using the SageMaker Jupyter notebook. Now, I want to replicate the same thing on AWS lambda.
1) How to run python code on AWS lambda with package dependencies for Pandas, XGBoost and SHAP (total package size greater than 500MB). The unzipped deployment package size is greater than 250 MB, hence lambda is not allowing to deploy. I even tried using lambda function from Cloud9 and got the same error due to size restrictions. I have also tried lambda layers, but no luck.
2) Is there a way for me to run the code with such big packages on or through lambda bypassing the deployment package size limitation of 250 MB
3) Is there a way to trigger a SageMaker notebook execution through lambda which would do the calculations and return the output back to lambda?
I found the 250MB limitation on AWS lambda size to be draconian. Only one file ibxgboost.so from xgboost package is already around 140 MB which leaves only 110Mb for everything else. That makes AWS lambdas useless for anything but simple "hello world" stuff.
 As an ugly workaround you can store xgboost package somewhere on s3 an copy it to the /tmp folder from the lambda invocation routine and point your python path to it. The allowed tmp space is a bit higher - 500MB so it might work. 
I am not sure though if the /tmp folder is not cleaned between the lambda function runs though.
How to run python code on AWS lambda with package dependencies >500MB?