shadow-deployment

Graph-native platform for migrating legacy systems (Java 6/7, COBOL, .NET Framework) to modern stacks. Verifies behavioral equivalence per node with a six-gate evidence pipeline, runs old + new in parallel via a receipt-linked shadow bridge, emits cryptographically signed audit trails for regulators.

Shadow-permanent 1% + SLO-gated canary ramp for Next.js on Vercel. Reusable pattern with Claude Code skill, docs, and drop-in templates.

End-to-end ML lifecycle platform with reproducible training, policy-based promotion, progressive delivery, and rollback.

Production-grade Go runtime for OAuth proxying, MCP integration, shadow deployment, auditability, and future Hugo MCP migration.

Adversarial bot detection pipeline utilizing a hybrid spatial-temporal deep learning network (GraphSAGE + Bi-GRU). Includes LLM-powered multi-agent traffic simulation, weak labeling heuristics, active learning uncertainty sampling, and CALEB CGAN adversarial training.

A pure-Python shadow deployment and canary release router for ML models — failure-isolated shadow calls, deterministic hash-based traffic splitting, and statistical promotion gates in one library.

Progressive delivery controller for ML model services. Paired shadow analysis refuses behaviorally broken models in 5s with zero live exposure; staged 5/25/50% canary ramp with Wilson-bound gates and hysteresis rollback. Measured: 0 false rollbacks in 10 identical-model rollouts; +100ms regression caught at 5% traffic.

Production-style Machine Learning Shadow Deployment Platform using React, FastAPI, Docker, Kubernetes and Shadow Deployment.

Production-style model inference platform — versioned registry, dynamic micro-batching (measured 8x throughput), canary + shadow deployments with instant rollback, load shedding (429+Retry-After), Prometheus metrics, async load-test harness

Production-grade async middleware for shadow testing ML models. Features real-time traffic forking, drift detection (latency/accuracy), and automated regression suite generation.

End-to-end fraud detection: XGBoost training, Go ONNX serving, drift detection, shadow mode, and canary deployment with automated rollback.

Progressive rollout, shadow mode, and auto-rollback for AI agents. Sticky-percent routing with promote/rollback gates driven by real metrics. Platform engineering reliability for the agent era.