ML in Production – Serverless and Painless
ML in Production – Serverless and Painless

Abstract: 

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 will walk through some of the best serverless options on how to operationalize 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 performance ML models to mobile users.

Bio: 

Oliver Gindele is head of machine learning at Datatonic. Oliver is passionate about using computer models to solve real-world problems. Working with clients in retail, finance, and telecommunications, he applies deep learning techniques to tackle some of the most challenging use cases in these industries. He studied materials science at ETH Zurich and holds a PhD in computational physics from UCL.