Developing and Deploying AI/ML Applications on Red Hat OpenShift AI is a course for data scientists and ML engineers who work with Red Hat OpenShift AI to develop and deploy machine learning solutions at scale. Students learn to use JupyterHub notebooks on OpenShift, build and train ML models using open-source frameworks, deploy trained models as REST API serving endpoints, and manage the end-to-end ML lifecycle on a Kubernetes-based platform.
What You Will Learn
- Set up and use JupyterHub notebooks within Red Hat OpenShift AI for ML development
- Train ML models using frameworks including scikit-learn, TensorFlow, and PyTorch on OpenShift
- Use OpenShift AI Pipelines to build and orchestrate reproducible ML training workflows
- Deploy trained models as REST serving endpoints using OpenShift AI’s model serving capabilities
- Monitor deployed model performance and manage model versions in the OpenShift AI environment
Who Should Attend
Data scientists, ML engineers, and AI developers who deploy machine learning solutions on Red Hat OpenShift or Kubernetes-based platforms.
Prerequisites
Python programming experience and familiarity with machine learning concepts. Basic Kubernetes or OpenShift familiarity is helpful.
