Experience MLOps through Red Hat’s proven open culture and practices, and learn how to move machine learning models into production more reliably, efficiently, and collaboratively.
MLOps Enablement with Red Hat AI Enterprise (AI500) is a five-day immersive program designed to help teams experience and implement a successful MLOps adoption journey.
While many AI and data science courses focus on a specific framework or technology, AI500 explores how multiple open source tools work together across a complete MLOps workflow. The course combines continuous discovery, continuous training, and continuous delivery in a highly interactive format that reflects real-world machine learning delivery scenarios.
The experience is intentionally cross-functional. Data scientists, machine learning engineers, platform engineers, architects, and product owners work beyond their traditional silos and collaborate as a shared delivery team. This approach demonstrates how technical practices and team collaboration work together to improve innovation and delivery efficiency.
The course is based on Red Hat OpenShift AI, Red Hat OpenShift GitOps, and Predictive AI.
Benefits for Organizations
Many organizations find that their existing structures and machine learning practices are not sufficient to deliver AI-driven transformation outcomes such as faster model deployment, continuous improvement through feedback loops, and solutions that remain aligned with user needs.
Achieving these goals requires organizations to make:
- Collaboration,
- Automation,
- Lifecycle management,
- Continuous training,
- Continuous delivery
part of their standard machine learning operating model.
AI500 introduces real-world MLOps culture and modern delivery practices through hands-on experience.
Participants build a predictive machine learning model using Red Hat OpenShift, Red Hat OpenShift AI, and industry-standard MLOps software, tools, and techniques.
This approach can help organizations:
- Reduce the time required to deploy new models.
- Establish repeatable and standardized MLOps workflows.
- Monitor model quality more consistently.
- Improve alignment between technical teams.
- Scale AI transformation initiatives in a more controlled way.
Benefits for Participants
After completing AI500, participants should have practical experience with MLOps culture, modern MLOps practices, and the end-to-end process of bringing a machine learning model into production.
Participants will be able to:
- Apply MLOps principles to streamline machine learning development and deployment.
- Understand the complete lifecycle from inner-loop experimentation to outer-loop operations.
- Manage experiment tracking and model deployment processes.
- Build automated training pipelines.
- Establish continuous training and automated deployment workflows.
- Apply model monitoring practices.
- Improve traceability through dataset versioning.
- Use advanced deployment approaches such as canary and blue-green releases.
- Apply feature store concepts.
- Extend security guardrails into machine learning workflows.
- Improve collaborative development skills through pair and mob programming practices.
