ML infrastructure needs to support rolling model upgrades to ensure that updates to models can be deployed efficiently and without causing disruptions to ongoing production workflows. This capability provides a smoother, more reliable approach to integrating improvements or changes to models without negatively impacting the system or the end user experience.
Here are the key reasons why rolling model upgrades are essential:
1. Minimizing Downtime
Rolling upgrades allow models to be updated incrementally across the system rather than replacing the entire model at once. This helps minimize or eliminate downtime, ensuring that the model remains available and functional throughout the upgrade process. As a result, production systems can continue to serve predictions without interruptions, reducing the risk of downtime or unavailability.
2. Risk Mitigation
Introducing changes to ML models can sometimes lead to unforeseen issues or degrade performance in some scenarios. Rolling upgrades allow you to test the new model version on a subset of traffic or data before fully deploying it across the system. This provides an opportunity to identify and address any issues early in the process, thereby minimizing the impact on the entire system.
3. Seamless User Experience
For production systems that rely on ML models (such as recommendation engines, fraud detection,