Continuous delivery for machine learning workloads

Continuous delivery for machine learning (ML) workloads presents unique challenges compared to traditional software due to the dynamic nature of ML systems, which change across code, model, and data axes. Unlike static APIs or web services, ML models can degrade without code changes, as data distribution shifts can lead to worse predictions, making conventional CI/CD workflows inadequate. To address these challenges, ML teams require mechanisms such as feature flags to control model deployment and traffic routing, allowing for safe testing and gradual rollouts. Continuous monitoring for concept drift and model performance, along with automated retraining and rollback capabilities, are essential to maintain model quality. The article highlights the importance of separating deployment from release, using feature management platforms to mitigate risks, and adopting trunk-based development to support rapid experimentation with minimal disruption. These practices ensure that ML deployments are both flexible and resilient, accommodating the inherent uncertainties of working with evolving data and models.