Fire Prediction
Build an image pipeline to detect fire in facility camera feeds using Lake and ML.
Business problem
Utility companies with remote equipment need automated fire alerts. Manual monitoring of live feeds is expensive and error-prone.
Heimdall solution
Ingest facility images into Lake bronze via unstructured ingest, publish gold feature sets, and train a classification model in Heimdall ML — without hand-built feature pipelines.
Business impact
Real-time fire detection automates monitoring with higher accuracy and lower cost than manual review alone.
How it works
Ingest images into Lake
Load camera batches into bronze with unstructured ingest and catalog metadata.
Train ML classification
Heimdall ML extracts features and trains fire vs no-fire models on gold data.
Deploy detection APIs
Score incoming images through production REST endpoints.
See Heimdall in action
Describe your image monitoring use case
Key benefits
Automated monitoring
Reduce manual feed watching
Rapid iteration
Retrain when new labeled images arrive
Cataloged inputs
Trace which image batches trained each model
No custom CV code
Feature extraction handled in ML product
Scalable scoring
APIs handle high-volume camera feeds
Continuous improvement
Update models as data grows
Related use cases
More scenarios on the use cases hub.