Artificial Intelligence

In this section, you will explore the different stages of a model’s lifecycle with Red Hat OpenShift AI.

This section includes:

Connection : Access the OpenShift AI dashboard and set up your environment
Environment Setup : Set up your projects, data connections, and Pipeline Servers
Workbench Creation : Launch your Jupyter environment with a custom image
Model Training : Build and execute AI pipelines with GPU acceleration
Model Serving : Deploy your trained model as a REST API endpoint for inference

You will work on traffic sign detection using Computer Vision and Transfer Learning, while applying MLOps best practices for deployment.
By the end of this section, you will have developed a complete AI pipeline, from data preparation to model deployment.