Build Stuff'16 Lithuania

Have you ever wondered where your favorite feature came from? Was it influenced by a feature in another language? How are the different programming languages even related? I spent a couple months researching the history of some programming languages, and wanted to share that with you. In this talk, I cover the history of the ML family from approximately the dawn of time, eventually focusing on F# specifically.

Just like traditional applications development, machine learning involves writing code. One aspect where the two differ is the workflow. While software development follows a fairly linear process (design, develop, and deploy a feature), machine learning is a different beast. You work on a single feature, which is never 100% complete. You constantly run experiments, and re-design your model in depth at a rapid pace. Traditional tests are entirely useless. Validating whether you are on the right track takes minutes, if not hours.
In this talk, we will take the example of a Machine Learning competition we recently participated in, the Kaggle Home Depot competition, to illustrate what "doing Machine Learning" looks like. We will explain the challenges we faced, and how we tackled them, setting up a harness to easily create and run experiments, while keeping our sanity. We will also draw comparisons with traditional software development, and highlight how some ideas translate from one context to the other, adapted to different constraints.

Want to simplify state management, improve scalability and reduce the code base? Rewrite your system in F# and use the actor model (Akka.NET). The functional language discourages use of mutable state and actors contribute to efficient communication, routing and scalability. And what about the code base? The code metrics speak for themselves. This talk is about real-world project and summarizes experience writing a robust and performant message based system for distributing media files to the cloud.