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Predict equipment failures and forecast business metrics on AWS using Amazon SageMaker, time-series models, and IoT or enterprise data - with actionable alerts and dashboards.
Overview
Turn operational and business data into forward-looking insight. Our engineers design and implement predictive maintenance and forecasting solutions on AWS that combine time-series modeling, anomaly detection, and domain-specific feature engineering - applied both to IoT sensor telemetry (vibration, temperature, current, pressure, acoustic, utilization) and to non-IoT data sources such as ERP, CRM, point-of-sale, SCADA historians, supply-chain systems, and financial ledgers.
The architecture spans ingestion, modeling, and action. Sensor data flows through AWS IoT Core, AWS IoT SiteWise, Amazon Kinesis, or MQTT gateways; enterprise data is integrated via AWS Glue, Amazon AppFlow, and direct database connectors into Amazon S3 and the AWS data lake. Feature engineering, model training, evaluation, and deployment run on Amazon SageMaker - including SageMaker Canvas for business analysts and SageMaker AI with DeepAR, Prophet, XGBoost, or custom deep-learning models for engineering teams. We deliver production MLOps with SageMaker Pipelines, model monitoring, drift detection, and automated retraining, plus alerting via Amazon EventBridge and SNS and dashboards in Amazon QuickSight or your existing BI tool. Typical outcomes include reduced unplanned downtime, optimized maintenance windows, improved demand and revenue forecasting, better inventory and workforce planning, and anomaly detection for security, fraud, and quality use cases.
This offering relates to the following AWS services: Amazon SageMaker (including SageMaker AI, SageMaker Canvas, SageMaker Pipelines, and SageMaker Model Monitor), Amazon Bedrock, Amazon Lookout for Equipment, AWS IoT Core, AWS IoT SiteWise, AWS IoT Greengrass, Amazon Kinesis Data Streams, Amazon Managed Service for Apache Flink, AWS Glue, AWS Lake Formation, Amazon S3, Amazon Athena, Amazon Redshift, Amazon Timestream, Amazon EventBridge, Amazon SNS, Amazon QuickSight, and Amazon CloudWatch.
Highlights
- Predictive maintenance on IoT telemetry — vibration, temperature, current, pressure, and acoustic signals — using Amazon SageMaker, Amazon Lookout for Equipment, AWS IoT Core, and AWS IoT SiteWise, with edge inference on AWS IoT Greengrass for latency-sensitive factory, utility, and asset-intensive environments.
- Time-series forecasting across IoT and enterprise data sources — demand, revenue, energy, supply chain, workforce, inventory — using Amazon SageMaker with DeepAR, Prophet, XGBoost, and custom deep-learning models, plus SageMaker Canvas for no-code forecasting by business analysts.
- Production MLOps with SageMaker Pipelines, model monitoring, drift detection, and automated retraining — plus actionable alerts via Amazon EventBridge and SNS and dashboards in Amazon QuickSight, so predictions become operational decisions rather than reports.
Details
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Delivery method
Deployed on AWS
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Support
Vendor support
Our Data Science and MLOps Professional Services team delivers and supports each engagement end-to-end — from discovery and architecture, through implementation and go-live, to knowledge transfer and post go-live support scoped to customer requirements.
Contact:
- Email: aws@brinel.ro
- URL: <www.brinel.ro/en/contact >