Operationalizing AI/ML Applications with OpenShift AI

Bringing AI and machine learning models into production can be complex, requiring robust infrastructure and efficient deployment strategies. This course demystifies the process of taking your AI/ML projects from experimentation to operational readiness. By completing this course, you will acquire the foundational knowledge and practical understanding to develop, deploy, and manage AI/ML applications effectively on OpenShift AI. You will learn to navigate the complete application lifecycle, ensuring your models are not only built but also efficiently integrated and maintained in a production environment. What you'll learn: Understand the core concepts of AI/ML application development and the OpenShift AI ecosystem. Learn to containerize AI/ML models and applications for consistent deployment. Apply foundational MLOps principles to manage the AI/ML application lifecycle, from development to deployment. Develop strategies for deploying and scaling AI/ML models for production use cases. Practice monitoring basic performance and health of deployed AI/ML applications. Configure essential resources within OpenShift AI for building and serving your models. This course begins with an introduction to key terminology and foundational concepts, gradually progressing through the stages of AI/ML application development and deployment. You will then explore practical aspects of operationalizing these applications within the OpenShift AI environment. This course is designed for absolute beginners interested in deploying AI/ML solutions, with no prior experience in OpenShift AI or MLOps required. Begin your journey to confidently operationalize AI/ML applications today.