Working with moderate numbers of sparse time series is pretty straightforward using standard tools like pandas, but increasingly I have to work with large numbers of sparse time series in AWS Athena / PrestoDB and so those handy pandas functions aren't always available.…
Amazon EMR seems like the natural choice for running production Spark clusters on AWS, but it's not so suited for development because it doesn't support interactive PySpark sessions.…
Currently, SageMaker supports Python 3.6 kernels only, which means you can't run any 3.7 or 3.8 code out of the box. Fortunately, there's an easy fix.…
SageMaker uses conda for package management, which complicates things if you manage packages for your project with pipenv. One quick workaround is to use pipenv to generate a requirements.txt file and pipe the output to pip which then modifies the active conda environment.…