Course Overview
Experience the possibilities of MLOps through proven open culture and practices used by Red Hat to support customer innovation.
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MLOps Practices with Red Hat OpenShift AI (AI500) is a five-day immersive class that guides attendees through a complete MLOps adoption journey. Unlike trainings focused on a single framework or tool, it demonstrates how leading open-source technologies integrate into a full MLOps workflow, blending continuous discovery, training, and delivery in realistic machine learning scenarios.
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Cross-functional participation is essential for achieving the learning goals. Data scientists, ML engineers, platform engineers, architects, and product owners collaborate in a simulated real-world delivery environment. This daily routine shows how breaking down silos and working as a unified team drives innovation, equips participants with shared best practices, and strengthens organizational culture and processes.
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The course is built on Red Hat technologies, specifically Red Hat OpenShift AI, Red Hat OpenShift GitOps, and Predictive AI, providing a practical foundation for applying modern MLOps methodologies.
What are the skills covered
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This course takes you an end to end journey of a Predictive Intelligent Application use case, from ideation to inner loop experimentation to production, while bringing different personas together to seamlessly collaborate on a single platform.
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This course blends cultural and technical practices into a unique, highly-engaging experience, packed with real-world applications. You will learn MLOps practices and how they build upon each other to improve team alignment and delivery efficiency.
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Most AI training focuses on a particular framework or technology, this course combines the best Open Source tools while giving you the experience of how they fit together to reliably and efficiently build, deploy and maintain AI models in production.
Who should attend this course
This experience demonstrates how individuals across different roles must learn to share, collaborate, and work toward a common goal to achieve positive outcomes and drive innovation.
It is especially valuable for:
- MLOps Platform Users: Data scientists, data engineers, and application developers.
- MLOps Platform Providers: Machine learning engineers, MLOps engineers, and platform engineers.
- MLOps Platform Stakeholders: Architects and IT managers.
The scenario incorporates technical aspects of working with machine learning systems, offering practical insights into how these roles can align their efforts.
Course Curriculum
What are the Prerequisites
- Take our free assessment to gauge whether this offering is the best fit for your skills.
- Containers, Kubernetes and Red Hat OpenShift Technical Overview (DO080) or Basic understanding of OpenShift/Kubernetes and containers is helpful
- High level understanding of AI or Red Hat AI Foundations is beneficial
