In its most basic definition, machine learning is a tool that makes takes a data set, finds a correlation in that data set, and uses that correlation to improve a system. Any complex system with well-defined behavior and clean data can be improved with machine learning.

Several precipitating forces have caused machine learning to become widely used: more data, cheaper storage, and better tooling. Two pieces of tooling that have been open sourced from Google help tremendously: Kubernetes and TensorFlow.

Kubernetes is not a tool for machine learning, but it simplifies distributed systems operations, unlocking more time for engineers to focus on things that are not as commodifiable–like tweaking machine learning parameters. TensorFlow is a framework for setting up machine learning systems.

Machine learning should affect every aspect of our lives–including tuxedo fitting. Generation Tux is a company that allows customers to rent apparel that historically has required in-person fitting. Using machine learning, they have developed a system that allows customers to get fit for an outfit without entering a brick-and-mortar store.

In this episode, Colan Connon and Thomas Bell from Generation Tux join to explain how Generation Tux adopted Kubernetes and TensorFlow, and how the company’s infrastructure and machine learning pipeline work.

The post Apparel Machine Learning with Colan Connon and Thomas Bell appeared first on Software Engineering Daily.