Used for storing results of users (malaria predictions) and displaying to user in the app. Although the realtime aspect wasn't huge in this project, it was much quicker to push data elements for each user as firebase elements since they were purely numerical and very small. And again, the idea of familiarity - I've worked with Firebase at previous hackathons, so no need to spend time going through docs, just straight to the coding.

Backend server for analysis of image samples from iPhone microscope lens. Chose this because of familiarity. The number one thing that I've learned at hackathons is that work exclusively with what you're 100% comfortable with. I use Python extensively at my day job at Wit.ai, so it was the obvious choice for the bulk of my coding.

I used both scikit-image and OpenCV for image processing and cell identification on the backend. Trained to identify malaria cells based on image datasets online. When it comes to quick training for image processing, OpenCV and scikit-image are the two best choices in my opinion. The approach I took to cell detection was template-matching and edge detection based. Both are highly tested and very powerful features of the Scikit Image and OpenCV libraries, and also have great Python interfaces.