Multi-Regional Geospatial ML Infrastructure

Built multi-region GPU inference and 1TB+/day ETL pipelines for satellite imagery at Pixxel, which improved inference efficiency by 75% and cut map-rendering latency in half.

Processed daily
1TB+
Inference-efficiency improvement
75%
Lower map-rendering latency
50%
How the multi-regional geospatial ML infrastructure moves data to maps Over 1 TB of daily satellite data flows through schema-checked ETL into a distributed GPU queue; automated A/B testing routes each region to its best model before feeding low-latency map rendering. Geospatial data (1TB+ daily) flows to ETL pipelines (Schema handling) [1TB+/day]; ETL pipelines (Schema handling) flows to GPU queue (Scheduling); GPU queue (Scheduling) flows to Regional GPUs (Inference) [jobs]; A/B testing (Regional models) drives Regional GPUs (Inference) [selects]; Regional GPUs (Inference) flows to Map rendering (Latency-sensitive). Owned by Sudhanva: ETL pipelines, GPU queue and A/B testing. Outcomes: Inference efficiency: Baseline to +75%; Map-rendering latency: Baseline to −50%; Schema-caused failures: Baseline to −30%. MULTI-REGIONAL INFRA 1TB+ DAILY Geospatial data SCHEMA HANDLING ETL pipelines SCHEDULING GPU queue REGIONAL MODELS A/B testing INFERENCE Regional GPUs LATENCY-SENSITIVE Map rendering 1TB+/day jobs selects INFERENCE EFFICIENCY Baseline +75% MAP-RENDERING LATENCY Baseline −50% SCHEMA-CAUSED FAILURES Baseline −30% Owned by Sudhanva Data Control
How the multi-regional geospatial ML infrastructure moves data to maps MULTI-REGIONAL INFRA 1TB+ DAILY Geospatial data SCHEMA HANDLING ETL pipelines SCHEDULING GPU queue REGIONAL MODELS A/B testing INFERENCE Regional GPUs LATENCY-SENSITIVE Map rendering 1TB+/day jobs INFERENCE EFFICIENCY Baseline +75% MAP-RENDERING LATENCY Baseline −50% SCHEMA-CAUSED FAILURES Baseline −30% Owned by Sudhanva Data Control
Over 1 TB of daily satellite data flows through schema-checked ETL into a distributed GPU queue; automated A/B testing routes each region to its best model before feeding low-latency map rendering.

Confidentiality note. Internal service names, proprietary scientific details, and employer-specific implementations are kept general here. Every scale figure, responsibility, technology, and outcome below comes from already-published facts in Sudhanva’s résumé and public profile.

Problem

Pixxel’s satellite pipeline ingested over 1 TB of hyperspectral imagery a day across multiple regions and served map tiles to users. Running every job through a single central GPU queue wasted cross-region bandwidth, slowed down map rendering, and broke whenever upstream metadata schemas changed.

Scale and constraints

Scale

  • 1TB+ of satellite imagery and geospatial data processed daily across ETL and ML pipelines.
  • Multi-region cloud deployment placing GPU inference in the same regions as ingested imagery.
  • Distributed GPU job queue with automated regional A/B model evaluation.

Constraints

  • Route imagery to the matching regional model and GPU pool automatically.
  • Reduce map-rendering latency and cloud transfer costs without dropping model accuracy.
  • Catch upstream metadata and schema changes before they break daily ingestion and inference runs.

Sudhanva’s ownership

Sudhanva designed the multi-region ML infrastructure, built the daily ETL and inference pipelines, and wrote the distributed GPU job scheduler.

  • Multi-region cloud and GPU inference setup.
  • Daily geospatial ETL and ML data pipelines.
  • Distributed GPU queue and automated A/B model selection per region.

Architecture at a safe level

Internal service names and proprietary scientific details are omitted below, while the data flow, orchestration, and operational setup reflect the production system.

  • Deployed autoscaling GPU workers in the cloud regions where satellite downlinks landed so terabytes of rasters stayed local during inference.
  • Built a distributed GPU job scheduler with automated A/B evaluation that selected the best model for each geographic region.
  • Added schema validation at ingestion to reject malformed metadata before GPU jobs started.

Engineering decisions and tradeoffs

  • Running inference across multiple cloud regions cut data-transfer time and egress spend, though it required managing deployments across regions in Terraform.
  • Tuned GPU queue autoscaling to balance job startup time against spot and on-demand budgets.

Results

  • Improved inference efficiency by 75%.
  • Reduced map-rendering latency by 50%.
  • Reduced pipeline failures caused by schema issues by 30%.
  • Saved $500,000 annually in cloud costs.

Reliability and operations

  • Ingestion schema checks stopped malformed payloads from crashing jobs mid-run.
  • Automated regional A/B routing replaced manual model swaps.

Transferable lessons

  • Validating raster metadata and schemas at ingestion caught 30% of pipeline failures before any GPU work started.
  • With 1 TB+ of daily satellite imagery, placing GPU workers in the ingestion regions was faster and cheaper than copying rasters to a central cluster.

Technologies

  • Geospatial ML
  • ETL
  • GPU scheduling
  • Multi-region cloud infrastructure
  • A/B testing

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