A deployment pipeline moves a validated AI model toward production through repeatable checks and promotion steps. In AI Foundry, a model can be published to a staging endpoint with one click or through a CI/CD workflow. section 7.4.5
A typical promotion can include:
- Approval gates that pause for a human reviewer to check indicators such as model quality or classifier accuracy. section 7.4.5
- Smoke tests that send sample inputs to staging before promotion continues. section 7.4.5
- A canary rollout that shifts a small share of live traffic, or a blue/green swap between parallel production environments. section 7.4.5
- Ongoing monitoring for latency, errors, request volume, data drift, fairness, and content-moderation accuracy. section 7.4.5
From model validation to rollout
For Banning Books, a validated book-recommendation or cover-classifier model can go to staging for smoke testing. After review and checks, a canary or blue/green rollout can limit the impact of problems. section 7.4.5
Figure 7.11 The Monitoring dashboard in AI Foundry when your model is first created
The monitoring dashboard illustrates the operational visibility available for deployed models. section 7.4.5
