quality_assurance · energy · workflow
Climate tech startups Orbital Materials and Hum.AI train foundation models for climate solutions with Amazon SageMaker HyperPod
Climate tech startups need to train complex foundation models on vast and diverse environmental datasets but face high infrastructure complexity, cost, and fault-tolerance challenges at GPU scale that slow down innovation.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Environmental data collection
Training is initiated on large-scale environmental datasets, including 50 years of historic satellite data amounting to thousands of petabytes.
Tools used
Amazon SageMaker HyperPodAmazon EKSAmazon CloudWatch Container InsightsAmazon Managed Service for PrometheusAmazon Managed Service for GrafanaAWS TrainiumAmazon BedrockAmazon SageMakerAmazon SageMaker JumpStartAWS MarketplaceAmazon EC2SlurmLlama 7B
Outcome
Orbital Materials achieved a tenfold improvement in material performance and Hum.AI achieved the ability to see underwater from space for the first time; SageMaker HyperPod enabled both teams to train larger models faster and reduced operational overhead.
Results
Time savedminutes rather than weeks
Volumetenfold improvement
Cost replacedsaves thousands in lost progress between checkpoints
Running sincefirst quarter of 2024
Grounding & classification
Source type: platform led case
38 fields verified against source quotes, 1 dropped as unverifiable.
computer visioncontent generationforecastingknowledge basehuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedenergymanufacturingsoftwareaccuracy improvementcost reductionemployee productivitytime savedplatform led casequality assuranceai draft human approval