Predictive models has significant leverage in business value, such as finding the right product faster to a client, understanding customer behavior, personalizing customer service, among others. However if they aren't deployed in a productive environment that allows to take decisions quickly, they might become useless. The goal of this presentation is show from scratch how a trained model can be deployed as a service in AWS.
At the end of this presentation you'll know how to dockerize your trained model application, then deploy it in a productive environment such as AWS and make it available to other services consume. In addition, it is a hands-on presentation and we'll walkthrough in reproducible scripts that deploys the model using docker and AWS command line clients.
Lia Bifano is pursuing a Master's degree at EPFL and in the last two years she worked with Nubank team as a Data Scientist. At Nubank she developed predictive models and ETL pipelines and her previous work experience was at Itaú Bank as a Business Analyst. She has BS (2012) in Statistics from Unicamp and at university she worked with models to predict joined market volatility using copulas and with MCMC methods for Bayesian inference.