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Course Outline
Foundations of AI-Enhanced Release Control
- Grasping the concepts of feature flags and progressive delivery.
- Core principles of canary testing and staged exposure.
- Identifying where AI adds value within release workflows.
Machine Learning Techniques for Rollout Decisions
- Establishing baselines for system and user behavior.
- Implementing anomaly detection methods for early warning systems.
- Addressing training data considerations and establishing feedback loops.
Designing AI-Driven Feature Flag Strategies
- Creating dynamic flag rules informed by AI signals.
- Defining exposure thresholds and automated score gates.
- Implementing adaptive logic for increasing, pausing, or rolling back features.
AI-Assisted Canary Analysis
- Comparing canary performance against baseline metrics.
- Weighting metrics and generating AI-based risk scores.
- Initiating automated decision pathways.
Integrating AI Models into Release Pipelines
- Embedding AI verification steps within CI/CD stages.
- Linking feature flag systems to ML engines.
- Managing pipelines to support hybrid automated and manual workflows.
Monitoring and Observability for AI Decision-Making
- Identifying the signals necessary for reliable AI inference.
- Collecting telemetry data on performance, crashes, and user behavior.
- Closing the feedback loop through continuous learning.
Risk Management and Operational Governance
- Ensuring responsible automation in release decisions.
- Defining conditions for human review and override points.
- Auditing actions taken via AI-driven rollout processes.
Scaling AI-Based Rollout Strategies Across Products
- Establishing multi-team governance frameworks.
- Standardizing reusable ML components and models.
- Normalizing cross-product telemetry data.
Summary and Next Steps
Requirements
- A solid understanding of CI/CD workflows.
- Practical experience with feature flags or deployment pipelines.
- Familiarity with fundamental statistical or performance monitoring concepts.
Target Audience
- Product engineers.
- DevOps professionals.
- Release engineers and technical leads.
14 Hours
Testimonials (1)
The course was very useful, and the trainer was clear, well-prepared, and engaging. I liked the fact that it was very practical with labs and real use cases. Overall, it was a valuable training experience.
