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Power BI Deployment Pipelines for Teams
How Power BI teams use deployment pipelines: stages and capacity requirements, which items can be deployed, item pairing and the duplicates it can create, deployment rules that point each stage at the right data, why data needs refreshing after deployment, and a team workflow.
Edge ML Deployment: Running Models on Devices
Edge deployment runs models on phones and devices instead of servers. With real smartphone sensor data, this guide exports an activity model to ONNX, quantizes it to a 39 KB file at almost no accuracy cost, and shows the bigger risk on devices is new people: accuracy ranged from 81% to 99.5% across volunteers.
Shadow Deployment and Canary Releases for ML
Shadow mode runs a new model on real traffic without letting it answer; a canary lets it answer for a few users first. On real e-commerce sessions, this guide shows a unit bug that 89% agreement hid, the flag-rate guardrail that caught it, and why per-stage business metrics were too noisy to gate on.
Model Deployment: From Notebook to Production
The steps between a model that works in a notebook and one people can rely on: choosing batch or online serving, turning the notebook into a reproducible pipeline, a concrete case of training-serving skew and how a saved pipeline prevents it, a small validated prediction API, and what to monitor after launch.