Canary a Model Swap in Foundry: Sticky Splits and Version Stamps
An offline evaluation can't tell you how a new model handles real traffic. How I'd canary a Foundry model change with sticky splits and per-call version logs.
8 articles
An offline evaluation can't tell you how a new model handles real traffic. How I'd canary a Foundry model change with sticky splits and per-call version logs.
Every Foundry model deployment has a version upgrade policy, and the default swaps your model at retirement. How to choose, audit and run model changes safely.
Configure autoscaling rules that balance responsiveness with cost for production deployments while maintaining service level objectives.
Automated pipelines ensure reproducibility, enable A/B testing, and provide audit trails for model governance.
Configure Azure Monitor alerts on latency and error rates. Implement automatic rollback triggers when thresholds are breached.
Best for: Variable workloads, quick start, minimal ops overhead Best for: Predictable workloads, SLA requirements, cost optimization
Best practices and patterns for deploying machine learning models in Microsoft Fabric.
Model deployment is where your ML work delivers business value. Azure ML's managed endpoints make it straightforward to deploy, scale, and update models in…