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 requires engineering across the full stack: data pipelines, model training, serving infrastructure, monitoring, and operational integration.
What You Get
- ▸Feature engineering from process and equipment data
- ▸Neural network and statistical models for defined failure modes or process deviations
- ▸FastAPI services for production inference
- ▸Anomaly detection and condition monitoring
- ▸Experiment tracking, model registry, drift monitoring, and retraining workflows
- ▸Operator-facing analytical outputs and system documentation
Stack & Tools
Python, PyTorch, scikit-learn, statsmodels, FastAPI, MLflow, Databricks, Delta Lake, Apache Airflow, Docker, and Kubernetes. The implementation is selected for the data, operating environment, and maintainers.
How We Work
Phase 1
Identify
Identify a costly or unreliable workflow and understand the systems, people, and data involved.
Phase 2
Define
Set a bounded project scope, practical constraints, and a measurable outcome before development begins.
Phase 3
Build and Validate
Build the solution in visible increments and validate it with representative data and the people who will use it.
Phase 4
Document and Handoff
Deliver working software, operating guidance, and documentation that support a clean handoff.
Right for You If…
- ✓You have historical data covering equipment failures or process deviations
- ✓A predictive-maintenance or analytical effort has stalled before production deployment
- ✓You need the pipeline and serving infrastructure around a model, not only a notebook
- ✓The intended users and operational response to a prediction are clearly defined
What You'll Need to Bring
- ▸Historical data with sufficient coverage of the target behavior
- ▸A subject matter expert who can participate in feature definition and validate outputs
- ▸Defined failure modes or process deviations and a clear response when a prediction occurs
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