I’ve a python serverless project that relies on scikit-learn, pandas, numpy and sqlachemy.
Combined size of these package is around 350MB which is 100MB more than allowed layer size. Is there any way to use serverless lambdas and provide these packages runtime?

is there a way to optimize pip install or other aws tewak to have this 350mb packages available for lambda reference. One way is to create a container and use to instantiate lambda. I don’t want this as the problem with this approach is that every time code is changed, image needs to be build and published.