Curriculum
How the program is structured
AI Deployment Fundamentals
Containerising and deploying AI services, with attention to cost and latency.
Infrastructure & Monitoring
Building ML pipelines and implementing monitoring for model performance and drift.
Reliability & Evaluation
Rollback and incident response procedures for AI systems, and evaluation methodology.
Capstone & Verification
Build a practical project end-to-end and complete the role-specific technical assessment.
What you'll learn and who this is for
Full Skill List
Prerequisites
Working experience with CI/CD pipelines, containerisation and cloud infrastructure.
By the end, you'll be able to
- Build CI/CD pipelines for AI and machine learning models.
- Implement monitoring and alerting for model performance and drift.
- Deploy AI services with attention to reliability and rollback procedures.
- Talk through AI infrastructure design tradeoffs in a technical interview.
Who this program is for
- DevOps and platform engineers with existing CI/CD and cloud infrastructure experience.
- Engineers moving toward MLOps or AI infrastructure specialisation.
Projects, assessment and verification
Practical Projects
- Production AI API
- Enterprise RAG Assistant
Assessment
Technical assessment covering AI deployment, infrastructure design and system monitoring.
Verification
Verified ITtoAI profile with assessed skills and reviewed project submissions.
Career Relevance
Aligned with MLOps and AI infrastructure hiring requirements at technology companies.
Completing this program does not guarantee employment, placement or salary outcomes. It builds practical capability and an evidence-based profile that demonstrates it.