Blue/green deployment is a strategy that minimizes downtime by running two identical production environments, referred to as "Blue" (the current version) and "Green" (the new version). Once the Green environment is fully tested and verified, live traffic is redirected from Blue to Green. The switch is nearly instantaneous. If any issues arise with the Green environment, traffic can be quickly reverted to the Blue environment. Azure ML online endpoints support this strategy by allowing you to deploy a new model version and then shift 100% of the traffic to it once it's confirmed to be stable.

A rolling deployment updates instances gradually, which can lead to a period where both versions are serving traffic, but it doesn't eliminate downtime as cleanly as a blue/green switch.

A canary deployment routes a small subset of traffic to the new version for testing, focusing on risk mitigation rather than primarily minimizing downtime for the full user base.

A shadow deployment runs the new model in parallel without affecting the user response. It is used for testing and validation, not for the live traffic switch itself.

1. Microsoft. (2023). Safe rollout for online endpoints. Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/how-to-safely-rollout-online-endpoints. (See the "Update endpoint with the new deployment" section, which describes how to shift 100% of traffic, enabling a blue/green strategy).

2. Fowler, M. (2010). BlueGreenDeployment. MartinFowler.com. Retrieved from https://martinfowler.com/bliki/BlueGreenDeployment.html. (This is a foundational software engineering resource describing the strategy, which is implemented by Azure ML's features).