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Duration 14 hours
Course Outline
Foundations of AI-Enhanced Deployment Workflows
- How AI enhances modern deployment practices
- An introduction to predictive deployment models
- Core concepts: drift, anomaly signals, and rollback triggers
Constructing Intelligent Deployment Pipelines
- Integrating AI components into existing CI/CD systems
- Data requirements for effective decision models
- Strategies for pipeline instrumentation
Risk Prediction and Pre-Deployment Analysis
- Assessing release readiness using machine learning
- Scoring models to gauge deployment risk
- Leveraging historical data for smarter rollout planning
AI-Managed Rollout Strategies
- Automating the selection of blue/green and canary releases
- Dynamically adjusting rollout speed
- Performing real-time risk scoring during deployment
Automated Rollback and Resilience Methods
- Understanding rollback triggers and thresholds
- Identifying anomalies via metrics and logs
- Coordinating rollbacks across distributed systems
Observability for AI-Driven Orchestration
- Gathering deployment telemetry to ensure model accuracy
- Designing efficient monitoring pipelines
- Correlating signals to refine decision automation
Governance, Compliance, and Safety Controls
- Safeguarding the auditability of AI-driven deployment actions
- Administering risk acceptance and approval policies
- Establishing trust mechanisms for automated decisions
Scaling AI-Orchestrated Deployments
- Architectures for multi-environment orchestration
- Integrating edge, cloud, and hybrid deployments
- Performance considerations for large-scale rollouts
Summary and Next Steps
Requirements
- Proficiency in CI/CD pipelines
- Experience with cloud-native deployment workflows
- Knowledge of containerisation and microservices
Target Audience
- DevOps engineers
- Release managers
- Site reliability engineers (SREs)
