As machine learning (ML) finds its way into more and more areas in our life,software developers from all fields are asked to navigate an increasinglycomplex maze of tools and algorithms to extract value out of massive datasets.In this talk we'll try to help the aspiring ML developer by describing: * a conceptual framework that most ML algorithms fall under * considerations about data readiness, algorithms, and software tools from an open-source perspective * some common mistakes and misconceptions in the development and deployment of ML systems The goal of the talk is to aid the audience to think about ML problems in anintegrated manner; facilitating the process of going from problem toprototype, making an informed choice about the algorithms and software to use,and providing examples of issues that can, and do come up in production. The talk is designed to be informative and entertaining, with little previousknowledge required.