Service
Predictive Analytics & Maintenance
Production ML in your industrial environment - from historian data through trained models to operational feedback loops.
The Problem
Unplanned downtime is expensive, but most predictive maintenance efforts stall before they reach production. A model in a notebook isn't a solution. Getting from historian data to a deployed inference service that operators trust and the control system can act on requires engineering discipline across the full stack - data pipelines, model training, serving infrastructure, and OT integration. That's the work this engagement delivers.
What You Get
- ▸End-to-end architecture: Historian - Pipeline - Model - Inference - OT Feedback
- ▸Feature engineering from process and equipment historian data
- ▸Neural network and statistical models trained on failure modes and process deviations
- ▸FastAPI microservices serving trained models alongside production data pipelines
- ▸Anomaly detection and condition monitoring with operator-facing alerting
- ▸MLOps infrastructure: experiment tracking, model registry, drift monitoring, retraining triggers
- ▸Operational dashboards surfacing model outputs in context operators can act on
- ▸Documentation covering the full pipeline - data sources through inference endpoints
Stack & Tools
OSI PI, Aspen IP.21, and Seeq (certified partner) as data sources. Python (PyTorch, scikit-learn, statsmodels), FastAPI, MLflow, Databricks, Delta Lake, Apache Airflow. Kubernetes and Docker for model serving infrastructure. Full MLOps delivery - not advisory.
How We Work
Phase 1
Discovery
Profile available historian and MES data, identify target failure modes or process deviations, and define the full architecture from data source to operational output. Align on what 'production-ready' means for this environment.
Phase 2
Design
Design the end-to-end system: feature engineering strategy, model architecture, inference service design, MLOps pipeline, and how outputs connect back to operators or control systems.
Phase 3
Build
Implement the full stack - data pipelines, model training, validation against historical events, FastAPI inference services, monitoring, and the operator-facing layer. Delivered as running infrastructure, not notebooks.
Phase 4
Enablement
Train your team on the deployed system: how to interpret outputs, monitor for drift, trigger retraining, and extend the pipeline to new assets or failure modes.
Right for You If…
- ✓You have historian data covering equipment that has experienced failures or process deviations
- ✓You've tried predictive maintenance and stalled before reaching production deployment
- ✓You need the full stack built - not just a model, but the pipeline and serving infrastructure around it
- ✓You want Seeq for process analytics alongside deployed ML and need a certified implementation partner
What You'll Need to Bring
- ▸Historical process data with sufficient coverage of the target failure modes (months to years, not weeks)
- ▸A process or reliability engineer who can participate in feature definition and validate model outputs
- ▸Defined failure modes or process deviations and a clear picture of what acting on a prediction looks like
Ready to get started?
Tell us where you are and what you're trying to solve. We'll let you know if we're the right fit.
Schedule a Consultation