The information age has led to influx of data, so, how do we take advantage of this? One term that is frequently used is ‘machine learning’. Machine learning techniques can support the development of intelligent applications for facial or speech recognition, pattern matching, fraud detection, driverless cars and many more. In this session I will attempt to show how Red Hat is bridging the gap between data scientists and developers, and, take you on my journey as a long term Java developer, to finding my feet as a data scientist. We will walk through the architecture design and implementation of my first OpenShift intelligent application. This will present the advantages and disadvantages of using microservice architectures with containers, specifically when combined with machine learning techniques.
The listener will come away with knowledge of:
This session is aimed at anyone who wants to get to grips with data science and how to deploy a cloud native application.