Data scientists spend a lot of time on data cleaning and munging, so that they can finally start with the fun part of their job: building models. After you have engineered the features and tested different models, you see how the prediction performance improves. However, the job is not done when you have a high performing model. The deployment of your models is a crucial step in the overall workflow and it is the point in time when your models actually become useful to your company.
In this session you will learn about various possibilities and best practices to bring machine learning models into production environments. The goal is not only to make live prediction calls or have the models available as REST API, but also what needs to be considered to maintain them. This talk will focus on solutions with Python (flask, Cloud Foundry, Docker, and more) and the well established ML packages such as Spark MLlib, scikit-learn, and xgboost, but the concepts can be easily transferred to other languages and frameworks.