GenAIOps and AI Application Delivery with Red Hat AI Enterprise

Scaling generative AI from a simple prototype to a production-ready enterprise application requires more than just training a model. It demands a robust operational framework that connects tools, pipelines, and applications seamlessly. This course prepares you to design and manage the entire Generative AI Operations (GenAIOps) lifecycle using Red Hat AI Enterprise. You will transition from treating AI as isolated models to deploying complete, secure, and scalable AI-enabled applications. What you'll learn: - Understand the core principles of GenAIOps and how they differ from traditional MLOps. - Configure Red Hat AI Enterprise environments to support the full AI application lifecycle. - Integrate vector databases and Retrieval-Augmented Generation (RAG) patterns into your workflows. - Deploy and scale generative AI models as production-ready application services. - Establish basic model monitoring, governance, and continuous integration pipelines. - Apply security best practices to protect sensitive enterprise data within AI workflows. You will start by mastering foundational GenAIOps terminology and architecture. From there, the text-based lessons guide you through configuring enterprise environments, managing model pipelines, and integrating modern patterns like RAG to deliver complete AI-driven services. This course is designed for IT professionals, system administrators, and developers looking to enter the field of AI operations, with no prior experience in machine learning required. Start reading today to build the skills needed to operationalize enterprise generative AI.