Edge AI and MLOps Solution for Seamless Model Deployment
Client: Global provider of edge cloud and fiber connectivity solutions | Solution: MLOps framework for versioned model deployment and edge AI orchestration
Sales teams needed consistent edge AI performance
Sales teams struggled with inconsistent model performance across different edge devices, leading to delays and inaccurate results. Without an automated rollout process, manual configurations and versioning inconsistencies impacted operational efficiency. Additionally, the lack of centralized feedback loops hindered continuous improvement.
Solution
Calsoft implemented an MLOps framework for optimized model deployment, centralized orchestration, and continuous feedback, ensuring consistent edge AI performance.
- Containerized ML packaging for optimized edge-device compatibility
- Lightweight edge orchestration engine for seamless rollout and updates
- Version registry for tracking model versions and retraining lineage
- Telemetry capture at inference for continuous performance analysis
Business Value
Model Deployment Agility
Zero-touch deployment allowed for on-demand updates across sites
Lifecycle Management
Centralized control provided visibility into model statuses and histories
Edge Performance Stability
Optimized models for constrained devices, improving service reliability
Feedback Loop Integration
Continuous model refinement with data-driven retraining cycles
To Know More
About how we can align our expertise to your requirements, reach out to us.