Hexagon accelerates AI model production with Amazon SageMaker HyperPod
Hexagon, the global leader in measurement technologies, collaborated with Amazon Web Services to scale AI model production for point-cloud workflows. The company built a managed training environment to pretrain state-of-the-art segmentation models for built-environment and geospatial use cases, with an integrated data pipeline, compute cluster management, and MLOps monitoring stack.
- Organization
- Hexagon
- Industry
- Manufacturing
- Location
- Switzerland
- Published
- February 2026
Reported outcomes
−95%
training timeTime & speed
Strategic outcomes
Primary read
Use case focus
Showing 2 of 2
- 1AI model training
- 2Training infrastructure modernization
- Hexagon needed to scale and accelerate pretraining of segmentation models for AI point-cloud workflows while maintaining high-performance training pipelines and reducing time-to-market for specialized AI models.
- The company also needed scalable compute resources, access to the latest GPUs, and more streamlined training operations.
- Hexagon implemented Amazon SageMaker HyperPod as a managed training environment for distributed model training.
- The architecture used Amazon S3 for training data storage, Amazon FSx for Lustre with data repository association for high-throughput data streaming and checkpoint export, Amazon EC2 GPU instances for training, Amazon SageMaker Training Plans for predictable GPU capacity, Amazon Managed Service for Prometheus and Amazon Managed Grafana for observability, and MLflow on Amazon SageMaker AI for experiment tracking.
- The HyperPod cluster included self-healing, automated job resumption, EFA-backed networking, lifecycle scripts, and integrated monitoring for cluster health and performance.
- Hexagon cut training time from about 80 days on-premises to about 4 days on AWS.
- The company achieved its first training deployment within hours.
- Larger batch sizes improved training throughput and model accuracy.
- Hexagon reported a 95% reduction in training time.
Architecture
Managed training environment on Amazon SageMaker HyperPod with self-healing cluster management, EFA-backed distributed networking, Amazon S3 and Amazon FSx for Lustre data pipeline, Amazon EC2 GPU instances, SageMaker Training Plans, Amazon Managed Service for Prometheus, Amazon Managed Grafana, and MLflow on Amazon SageMaker AI.
Sources & evidence1
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