- Course overview
- Course details
- Prerequisites
Course overview
About this course
This course offers attendees an opportunity to experience and implement a successful MLOps adoption journey. While many AI or data science training programs focus on a particular framework or technology, this course covers how the best Open Source tools fit together in a full MLOps workflow. It blends continuous discovery, continuous training, and continuous delivery in a highly engaging experience simulating real-world machine learning scenarios.
Audience profile
- 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.
At course completion
- Generative AI Fundamentals: Capabilities, Challenges, Models, and Techniques
- Granite Models For Enterprise Generative AI
- Training Large Language Models with Red Hat Enterprise Linux AI
- Deploying Trained Models with Red Hat Enterprise Linux AI
Course details
-
Module 1. What is MLOps?
-
Module 2. Inner Loop
-
Module 3.Training Pipelines
-
Module 4. Outer Loop
Module 5. Monitoring
Module 6. Data Versioning
Module 7. Advanced Deployments
Module 8. Feature Stores
Module 9. Security
Prerequisites
- Containers, Kubernetes and Red Hat OpenShift (DO080) or basic understanding of OpenShift/Kubernetes and containers is helpful
- High level understanding of AI or Red Hat AI Foundations is beneficial
Enquiry
Course : MLOps Enablement with Red Hat OpenShift AI
Enquiry
request for : MLOps Enablement with Red Hat OpenShift AI
