GOSHtastic - Game shows, Options, Software, & Hardware!

I'm happy to have some ideas worth blogging about at last!  Here's a really big one I stumbled across if you're in an organization that wants to keep permissions relatively locked down on AWS (in terms of performing actions on resources), but still wants to enable individual users the ability to interact with their own stacks as needed without waiting on admins. The TL/DR Of It Using AWS CloudFormation, you can deploy a stack consisting of a single resource.  This resource is of type AWS::IAM::ManagedPolicy .  Attach all the policy statements you want, then add Users consisting of any IAM users on the AWS account.  Voila, instant permissions to do whatever. More About the Process Let's say you need to tweak various aspects of your AWS setup.  Maybe you have a DynamoDB entry that needs to be manually tweaked, a Lambda function where the concurrency needs to be adjusted, an SQS queue where the maximum age of a message needs to be adjusted, or files sitting around...

How does one go about deploying a model on Amazon SageMaker from Keras, TensorFlow, or TensorFlow Hub without first doing training? There are countless articles and blog posts discussing how to train a machine learning model using TensorFlow or Keras, and then deploy that model right away to Amazon SageMaker.  But what if you're already starting from a SavedModel, or just want to serve up a model trained on plain vanilla ImageNet from within your own AWS account?  You might end up wading through tons of confusing, outdated information that will misguide you, causing you to go down rabbit holes that will make things seem unnecessarily complex.  For instance, you might be inclined to use compatibility libraries to find low-level attributes of modern classes to leverage older deprecated function calls, or to build totally unnecessary infrastructure along the side that makes a Docker container for your model, which will be inevitably broken because you don't know how to invok...