Pushing an ML docker container to AWS Lambda made easy with Serverless.

I previously deployed an ML inference on AWS via the Serverless framework and at the time I was using a scikit-learn model with a couple standard libraries...

A few lesser documented simple functions I've found handy along the way...

After tweaking an ML ensemble model for a few weeks it came time to deploy and in this case the project was utilizing an AWS pipeline. I had already dumped the trained model to an S3 bucket with joblib, so the goal now was to deploy an inference endpoint as an AWS Lambda function.

I’ve been meaning to try out the Intersection Observer API for some time now. I seem to have an ever-growing list of third-party scroll libraries, and the prospect of replacing all these with a native browser API is extraordinarily tempting.

For a small nuxt app I was looking to set up a simple JWT authentication scheme with Express and FaunaDB on the backend - makes sense to reach for nuxt/auth, the go-to solution for user management.

I was looking for the lowest friction solution to quickly to share my work with the team for review and collaboration. Currently, there are a lot of offerings for cloud-hosted notebooks and they all seem promising.