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. Note:  Beginner data handling and Python skills are required for this workshop.

1. A Workshop
Object Detection
on OpenShift
Prasanth Anbalagan
Senior Principal Software Engineer
Max Murakami
Specialist Solution Architect

2. 2
developers.redhat.com/register

3. Object Detection
3

4. Gather and prepare data Integrate models in app dev
Model monitoring
and management
Develop model
4
Red Hat OpenShift Data Science
RHODS Core
OpenShift-native services
Red Hat managed
cloud services
Open hybrid cloud platform with self service capabilities
Accelerators
Cloud infrastructure
Red Hat OpenShift Service on
Amazon Web Services
Red Hat managed
cloud platform
*
*post initial release
ISV managed cloud
services
Customer managed
ISV software

5. red.ht/rhods-sandbox
5

6. 6
console.redhat.com/application-services/streams

7. Deploying an intelligent application
data
scientist
ML model
S2I
public cloud on premise
user
https://red.ht/rhods-od-workshop

8. 8
Deploy object detection app
Explore Kafka integration
Explore object detection REST service
Object
Detection
on OpenShift
Explore object detection model in Jupyter Lab
Deploy real-time object detection app

9. redhat-scholars.github.io/rhods-od-workshop/od-workshop