Systematic approach to managing machine learning models deployed on edge devices, including versioning, A/B testing, performance monitoring, and remote model updates. Addresses unique challenges of ML in resource-constrained environments.
Investigate MLOps tools with embedded support and establish model versioning practices for edge ML projects
Critical as edge ML deployments become more sophisticated and require ongoing optimization