Torus for Docker-First Data Science

As interest in Artificial Intelligence (AI), and specifically Machine Learning (ML), grows and more engineers enter this popular field, the lack of de facto standards and frameworks for how work should be done is becoming more apparent. A new focus on optimizing the ML delivery pipeline is starting to gain momentum. Data scientists are becoming more involved in the delivery pipeline of products, and it is a non-trivial task ensuring that their work survives the delivery process. Of course, this isn't a new problem: in the past, traditional software development teams would throw their work "over the wall" to the operations team to serve in production with little to no context. A community effort to solve the inevitable mess resulted in what we now think of as DevOps, removing the wall between development and operations to drive increased efficiency and improve product quality.