DASA DevAIOps - Including Exam

Module 00: Programme Orientation

• Understand the learning path, assessments, and certification requirements.
• Explore DevAIOps maturity, current trends, and the growing need for AI skills in delivery teams.
• Review the FinTech case study covering release delays, rising costs, and AI readiness challenges.

Module 01: AI Fundamentals for DevOps Teams

• Learn key model types such as classification, regression, anomaly detection, and NLP.
• Understand model training, validation, drift, and their impact on reliability.
• Interpret AI outputs including alerts, confidence scores, and code suggestions.

Module 02: Intelligent CI/CD Pipelines

• Apply AI-assisted code review to improve quality and reduce manual effort.
• Use intelligent test prioritisation to balance speed, coverage, and risk.
• Enable predictive deployment gates, rollback logic, and self-healing responses.

Module 03: AI Augmented Observability and AIOps

• Detect issues earlier using anomaly detection and dynamic baselines.
• Reduce alert noise through event correlation and root cause analysis.
• Improve resilience with predictive scaling and faster incident response.

Module 04: MLOps & the AI Model Lifecycle in a DevOps Context

• Build model versioning, promotion workflows, and reproducible delivery processes.
• Monitor model performance, detect drift, and trigger retraining when needed.
• Use feature stores to improve consistency between training and live environments.

Module 05: DevSecOps with AI

• Apply AI-powered SAST, DAST, and dependency scanning across CI/CD pipelines.
• Use anomaly detection for runtime monitoring across containers and networks.
• Address AI-specific risks such as prompt injection, model poisoning, and data exposure.

Module 06: Platform Engineering and AI Structure

• Create self-service platforms and golden paths for AI delivery teams.
• Configure Kubernetes and GPU resources for training and inference workloads.
• Compare cloud and self-hosted options based on cost, latency, and compliance needs.

Module 07: AI Governance, Ethics and Regulatory Compliance

• Apply EU AI Act and GDPR requirements to technical delivery processes.
• Use model cards, lineage tracking, and documentation as code.
• Monitor fairness, define approval models, and manage AI incidents effectively.