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We help industrial energy enterprises implement scalable, production-grade MLOps pipelines tailored for Industrial IoT environments. Built on AWS technologies like SageMaker and IoT Core, our solutions automate model deployment, monitoring, and retraining across field, edge, and cloud systems, ensuring AI models for predictive maintenance, anomaly detection, and more stay accurate, auditable, and operational in real time.
Overview
In industrial environments, deploying AI models is only the beginning - the real challenge lies in maintaining, scaling, and updating those models reliably across thousands of connected assets. Without a robust MLOps infrastructure, teams struggle with drift, version control, retraining, and integration with OT systems, leaving valuable models underutilized or obsolete.
We design and deploy custom MLOps pipelines purpose-built for Industrial IoT environments, enabling continuous delivery, monitoring, and governance of AI models across field devices, edge gateways, and cloud systems.
Our solutions are built on Amazon SageMaker, enhanced with CI/CD tooling, version control, retraining workflows, and automated deployment triggers. We integrate model pipelines with real-time sensor feeds (via AWS IoT Core and SiteWise) and deliver observability dashboards for both data scientists and engineers. Whether for predictive maintenance, virtual flow metering, or anomaly detection, our MLOps frameworks ensure your models remain accurate, auditable, and production-ready.
Highlights
- Operational Continuity with Scalable AI: Ensure continuous, automated model updates and retraining across well sites, edge devices, and cloud environments—critical for real-time applications like virtual flow metering or predictive maintenance.
- Improved Model Accuracy & Compliance: Gain full observability with monitoring dashboards and drift detection tools, keeping models auditable and aligned with evolving field conditions and regulatory demands.
- Reduced Time to Insight: Integrate live sensor data streams into AI workflows, enabling faster, data-driven decisions with minimal latency across complex OT landscapes.
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