AI-500 MLOps with Red Hat OpenShift AI Training Course
Length
5 Days / 5 Weeks
Price
Days
Mon - Fri
Why Choose This Course
AI500 is an instructor-led, hands-on course focused on MLOps practices using Red Hat OpenShift AI. Learners work through an end-to-end journey, from ideation and inner-loop experimentation to production deployment and monitoring, using open source tools integrated on the Red Hat OpenShift AI platform. The experience brings together roles across data science, engineering, and architecture to simulate a real delivery team and build repeatable workflows for model development, deployment, and continuous improvement.
The course emphasises how to operationalise machine learning at scale on OpenShift AI, including collaborative workbenches, versioned data and experiments, automated training pipelines, CI and CD for models, responsible operations, and model serving in production. It is designed to help organisations accelerate the safe adoption of AI by aligning culture, practices, and platform capabilities.
AI500 is relevant for teams building AI-enabled applications and platforms in industries where reliability, governance, and collaboration matter. Participants gain practical skills that transfer directly to enterprise environments that use Red Hat OpenShift AI. A certificate of course attendance is included.
Prerequisites
There are no formal prerequisites for this course.
Exam
Candidates can achieve this certification by passing the following exam(s).
- Red Hat Certified Specialist in OpenShift AI exam (EX267).
Books
- AI500 course material included.
Delivery
- Live virtual online training attend in real-time from anywhere
Skills Gained
- Explain the purpose and core concepts of MLOps and how it differs from traditional DevOps in the context of AI systems.
- Use Red Hat OpenShift AI workbenches to develop and test models collaboratively.
- Organise data science projects, notebooks, and data connections for reproducible work.
- Track experiments and model versions to enable reliable comparison and rollback.
- Build and automate training pipelines to standardise model development for production.
- Package and deploy models to production using model serving on OpenShift AI.
- Implement monitoring for model performance and system health in production environments.
- Apply data versioning and lineage practices to support governance and reproducibility.
- Design outer-loop operations, including CI and CD patterns tailored for ML systems.
- Collaborate across roles using team-based workflows that break functional silos.
- Apply secure development and deployment practices for AI workloads on OpenShift.
- Align cultural practices with modern open source tools to accelerate AI delivery.
Audience
- Data scientists and AI practitioners building and iterating on models.
- Machine learning engineers, MLOps engineers, and platform engineers responsible for ML lifecycle operations.
- Application developers integrating models into intelligent applications.
- Architects and IT managers overseeing AI platform adoption and governance.
Course Schedule & Pricing
Choose the schedule that fits your life — all options include full course materials & certification support
Full-time immersion for rapid certification readiness.
Balance your career while you upgrade your skills.
Maximum flexibility for busy working professionals.
Outline
- MLOps fundamentals, principles, and culture for AI delivery teams.
- Inner-loop workflows for rapid model experimentation.
- Training pipelines for automating model development.
- Outer-loop operations and release patterns for ML systems.
- Monitoring models and services in production.
- Data versioning and lineage for reproducibility and auditability.
- Advanced deployment approaches for resilient model services.
- Feature stores and feature lifecycle management.
- Security considerations for AI workloads on OpenShift AI.
- Organising data science projects, workbenches, and access controls.
- Managing data connections for training and inference.
- Collaborative notebooks and Git-based workflows.
- Model registry concepts and version governance.
- Building CI and CD pipelines for models and data.
- Serving models as scalable services on OpenShift AI.
- Observability patterns for AI services and data pipelines.
- Managing users, resources, and quotas for multi-team environments.
- Applying responsible operations to maintain accuracy and drift awareness.
- Integrating open source tools into an end-to-end MLOps workflow.
- Team collaboration practices to accelerate model delivery.
- Lifecycle management from ideation to production and continuous improvement.
- Course wrap-up and next steps for platform and team enablement.
Terms & Conditions
Frequently Asked Questions (FAQ's)
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Our Partnership
Reliable certification testing is vital for validating professional skills in today’s tech-driven world. As a Pearson VUE Authorised Centre, we provide a secure environment for globally recognised IT exams. This partnership ensures convenient access to certifications with the highest standards of integrity and accuracy.
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