Sibros: a 75-billion-point/day SageMaker model + Bedrock AgentCore pipeline

Every platform company now carries a generative-AI ambition; few carry the production AI substrate it requires. For Sibros — a connected-vehicle OTA and telemetry platform ingesting on the order of 75 billion data points a day — the gap was concrete: telemetry economics needed a deployed predictive model with a contractual accuracy bar, and the longer-term AI roadmap needed what that first workload would force into existence — a governed data lake, a repeatable training path, managed inference with real observability, and a team able to operate all of it without a partner in the loop.

VeUP delivered the platform end to end on Amazon SageMaker. Training runs over a telemetry lake built on Amazon S3 with Apache Iceberg table format and Amazon Athena query access; AWS Step Functions and Amazon EventBridge orchestrate the pipeline; the production model serves through a SageMaker endpoint behind a REST API with IAM authentication and KMS encryption; Amazon CloudWatch carries the operational telemetry. Model choice honored the customer’s frugality constraint — deliberately simple algorithms (XGBoost, Random Forest, regression) that hit the bar without exotic compute. The first statement of work passed its contractual acceptance gate; a second extended the platform, including the generalization work that materially lifted held-out model quality; and a structured knowledge transfer moved independent operation to Sibros’s own engineering team. The result is the SageMaker estate — data, training, deployment, governance, and operating skill — on which the platform’s generative workloads land next.