Abstract: 

In this workshop, you’ll learn an easy way to incorporate data science and AI/ML into an OpenShift development workflow. As an example, you’ll use an object detection model to detect ‘dog(s)’ in an image.

You will:
Use Jupyter Notebooks and TensorFlow to explore a pre-trained object detection model
Serve the model in a REST API as a Flask App
Use Source-to-Image (S2I) to build and deploy the Flask app
Explore Kafka streams from Notebooks
Deploy a Kafka consumer with the same object detection model

You’ll be able to do all of this without having to install anything on your own computer, thanks to Red Hat OpenShift Data Science and Red Hat OpenShift Streams for Apache Kafka.

Background Knowledge
Note: Beginner data handling and Python skills are required for this workshop.
*Prior to the workshop, please sign up for RHODS Sandbox access at: Start using your OpenShift Data Science sandbox (https://developers.redhat.com/products/red-hat-openshift-data-science/overview)

Bio: 

Audrey Reznik has been in the IT industry (private and public sectors) for 27 years in multiple verticals. In the last 4 years, she worked as a Data Scientist at ExxonMobil where she created a Data Science Enablement team to help data scientists easily deploy ML models in a Hybrid Cloud environment. Audrey was instrumental in educating scientists about what the OpenShift platform was and how to use OpenShift containers (images) to organize, run, and visualize data analysis results. Audrey now works as a Data Scientist with the Red Hat OpenShift Data Science Team where she is focused on next-generation applications. She is passionate about Data Science and in particular the current opportunities with ML and Federated Data.