This session covers various deployment strategies for serving a python machine-learning model as an API. Many business applications can make good use of real-time scoring using machine learning, and one of the most approachable and easy languages to sue to build these models is Python. The goal is to show the audience how to actually take a trained python model and turn it into an API. We’ll start very simple and cover increasingly complex deployment strategies. Throughout, we will consider the API throughput and resource tradeoffs, and benchmark our solutions.
Henri Dwyer is a data scientist and engineer working on building the best platform for data scientists at Dataiku. Before, he did physics research on air pollution and solar cells. He has worked in a variety of industries ranging from marketing, pharmaceutical industry and transportation.