Multi-Regional Geospatial ML Infrastructure

Problem

Geospatial analytics combined large daily data volumes, regional model selection, GPU scheduling, and latency-sensitive map delivery. The platform needed to make those workloads efficient and dependable across regions.

Scale and constraints

Scale

• More than 1 TB processed each day by ETL and ML pipelines.
• Multi-regional infrastructure for geospatial analytics.
• GPU queue with automated regional model evaluation.

Constraints

• Coordinate compute and model selection across regions.
• Reduce latency and infrastructure cost while preserving analytical output.
• Prevent schema issues from repeatedly breaking data and ML pipelines.

Sudhanva's ownership

Sudhanva built the multi-regional ML infrastructure and data pipelines and architected the GPU queue.

• Multi-regional ML infrastructure.
• ETL and ML pipelines.
• GPU queue and automated A/B testing for regional model selection.

Architecture at a safe level

The public architecture is intentionally described through system responsibilities rather than internal service names or proprietary topology. The relevant boundaries were workload execution, orchestration, data movement, observability, and delivery.

• Designed regional infrastructure around the location and delivery needs of geospatial workloads.
• Automated model evaluation and GPU scheduling rather than relying on manual regional choices.
• Improved schema handling in the daily data path.

Important engineering decisions and tradeoffs

• Multi-regional execution improves proximity and resilience but increases operational coordination.
• GPU capacity and model-selection automation had to be weighed against cloud cost.

Results

• Improved inference efficiency by 75%.
• Reduced map-rendering latency by 50%.
• Reduced pipeline failures caused by schema issues by 30%.
• Saved $50,000 annually in cloud costs.

Reliability and operations

• Schema-focused pipeline improvements reduced a recurring class of failures.
• Automated regional model evaluation reduced manual operational choices.

Transferable lessons

• Data contracts are a reliability concern for ML systems, not only a data-engineering concern.
• Regional model delivery should be evaluated with latency, compute efficiency, and cost together.