Introduction
Software deployments often suffer from manual handoffs, unpredictable failures, and lengthy debugging. This assignment demonstrates how Six Sigma’s DMAIC (Define, Measure, Analyze, Improve, Control) framework can be supercharged with AI—leveraging NLP, ML, and predictive analytics—to transform CI/CD pipelines into proactive, self‑optimizing systems.
Key Highlights
- Define: Applied topic modeling on thousands of support tickets to pinpoint top issues (slow signup, API errors).
- Measure: Built automated data pipelines that ingest build/test metrics into time‑series stores for real‑time dashboards.
- Analyze: Used explainable ML (SHAP and clustering) to surface root causes and high‑impact code areas.
- Improve: Tuned infrastructure with genetic algorithms and generated GitOps manifests via LLMs in minutes.
- Control: Deployed forecasting models to predict resource exhaustion and trigger autoscaling before incidents.
Through concrete examples, this work shows how combining AI with DMAIC achieved near‑zero unplanned outages and sustained 98% deployment reliability.