Within the Wise.io team in GE Digital, we have monthly "edu-hackdays" where the entire tech team spends the entire day trying to learn and implement new promising approaches to some portion of our machine-learning based workflow. In the past, we worked on algorithm hacks and on methods for distributed featurization. Some of what we start those days eventually go into production, but most does not. The main goal (apart from the team building that comes with the fun and pain of all-day hacks) is to create collective knowledge and experience around important components of our stack. Recently we had an edu-hackday on strategies for distributed learning. This post captures (and hopefully provides some motivation for) the work I did at that hackday in April.
The architectural and organizational/process advantages of containerization (eg., via Docker) are commonly known. However, in constructing images, especially those that serve as the base for other images, adding functionality via package installation is a double edged sword. On one hand we want our images to be most useful for the purposes they are built but—as images are downloaded, moved around our networks and live in our production environments—we pay a real speed and cost price for bloated image sizes. The obvious onus on image creators is to make them as practically small as possible without sacrificing efficicacy and extensibility. This blog shows how we shrunk our images with a pretty simple trick...