Introduction to OpenShift AI for Data Science and MLOps

Organizations are rapidly adopting AI, but taking machine learning models from development to production requires a structured, scalable platform. OpenShift AI provides the essential tools to build, train, deploy, and monitor models efficiently at scale. In this text-based course, you will transition from understanding basic data science concepts to managing full lifecycle MLOps workflows. You will gain the skills to navigate Jupyter notebooks, configure data science pipelines, and serve models for real-world applications. What you'll learn: 1. Understand the core architecture of OpenShift AI and its role in modern MLOps. 2. Launch and configure Jupyter notebook environments for collaborative data science. 3. Build and automate data science pipelines to streamline model training and validation. 4. Deploy trained machine learning models as scalable APIs for application integration. 5. Apply modern MLOps best practices, including model monitoring and version control. 6. Integrate vector databases and foundational models to support retrieval-augmented generation (RAG) workflows. The course begins with foundational concepts of cloud-native data science before guiding you through hands-on, text-based walkthroughs of workspace setup, pipeline creation, and model deployment. You will learn through clear explanations and structured configuration examples designed for immediate application. This course is designed for aspiring data scientists, DevOps engineers, and IT professionals who are new to OpenShift AI, with no prior experience with container orchestration required. Start learning today and unlock the power of cloud-native machine learning operations.