Sabina Stanescu - Your First ML Model In Production: Examples & Considerations

As Data Science professionals, we want to do innovative, impactful work. Thus, our work on data munging and building machine learning models cannot happen in isolation from business objectives and the infrastructure of our organizations. In this talk, I will explore ways to identify impactful, executable Data Science work, and how to take this work to production.
I will discuss what it means to have a model in production, including ways to score the model in real-time versus batch. I will discuss sample architectures required to make model scores available for your application, such as through an API or database.
Finally, I will tie everything together with some of the processes and frameworks that allow for iteration and testing to complete the full life-cycle of model deployment. I will provide a real example of taking an ML project all the way from data capture to real-time scoring in production.