Edge-to-cloud AI deployment

Modern industrial environments demand AI systems that are fast, resilient, and scalable. Edge-to-Cloud AI architecture addresses these needs by combining real-time local processing with centralized model management and analytics. Industrial operations often face challenges like high data transmission costs, delayed cloud-based decision-making, limited network availability, and growing concerns about data privacy. These issues lead to slower responses, increased operational risks, and inefficient resource usage across critical sites.

We build AI models using Amazon SageMaker and optimize them for deployment on edge devices via AWS IoT Greengrass or SageMaker Edge Manager. This ensures immediate, low-latency decision-making close to the equipment, protects sensitive operational data by processing it locally, and reduces costs by sending only critical insights to the cloud.

Our typical architecture follows a hybrid model: AI/ML models are trained and retrained in the cloud (using Amazon SageMaker) for scalability and centralized governance. These models are compressed or optimized for deployment to edge devices via AWS IoT Greengrass or SageMaker Edge Manager. Edge devices perform real-time inference close to operational assets, ensuring low-latency responses and minimizing bandwidth usage. Only aggregated insights or detected anomalies are sent back to the cloud for further analysis and retraining cycles.

This architecture addresses key non-functional constraints such as:

Latency requirements: Critical actions (e.g., shutdowns, alerts) must happen in milliseconds, not seconds. Bandwidth optimization: Raw sensor data volumes are huge; preprocessing at the edge minimizes transmission costs. Data sovereignty and privacy: Sensitive operational data stays on-site, reducing regulatory risks.