ML in Production: Serverless and Painless

What you'll learn

Productionising machine learning pipelines can be a daunting and difficult task for Data Scientists. Fortunately, many novel tools and technologies have become available in the past years to address this issue and make it easier than ever to deploy ML models into production, without the need to configure servers. In this session, Oliver Gindele will walk through some of the best serverless options on how to operationalise ML pipelines within the Tensorflow ecosystem and on Google Cloud Platform based on actual case studies. One of these real-life case studies will dive into the journey of a global cosmetics brand to become packaging-free with the help of ML. The first step towards this goal allows customers to view product information simply by taking a picture. This completely eliminates the need for packaging and labels in stores. However, in order to do this effectively, an accurate image classification model, accessible on mobile phones, is needed. This session will cover the details of the end-to-end machine learning pipeline that was created to deliver and update performant ML models to mobile users.

Table of contents