TensorFlow has been open to the public for about a year now and the buzz is real. Everyone is building models using TensorFlow, and we even have some state of the art examples such as: Parsey McParseface. As a data scientist at Old Street Labs(OSL) one of the most common questions I'm asked is how we deploy models and manage those in a production environment. TensorFlow Serving makes it simple and quick to deploy your models into a production environment. It also works with TensorFlow models out of the box without much modification. The purpose of this tutorial is to provide a brief introduction into TensorFlow Serving by walking through the process of deploying a TensorFlow model inside a Docker container.