This article details a zero-downtime model update strategy using immutable artifacts, a model registry, canary deployments with readiness probes, traffic shifting via service mesh, and optional in-process generational hot loading, demonstrated with a GPU ranking service handling 600 QPS and 7 GiB models.
This article demonstrates a complete, reproducible ML pipeline using DVC for data versioning and MLflow for experiment tracking, covering immutable data snapshots, time-based splits, pipeline definitions, model registration gates, canary deployment, rollback procedures, CI integration, and retention policies — all validated with concrete code and failure-mode analysis.
A real‑world incident where a tiny Prompt tweak caused a two‑day outage illustrates why AI systems need CI/CD‑style versioning and gated release for six objects—code, Prompt, model, RAG index, tool schema, and workflow—so that changes are traceable, safely rolled out, and quickly reverted.
Lightning Flash 0.3 expands the PyTorch Lightning ecosystem with eight new computer‑vision and NLP tasks, modular API design, integrated model hubs, visualisation callbacks, customizable data‑source hooks, and a central registry for model backbones, all illustrated with concrete code examples.
