MACHINE LEARNING / DATA / PREDICTION
Signal
Suits
Data-rich operations
Focus
ML · SOFTWARE
Challenge
The data to predict demand, churn or failure already exists. It sits unused because models never make it out of the notebook.
Solution
A prediction pipeline built for production from day one: feature store, evaluation harness, monitoring for drift, and a deployment path that treats the model as a component rather than a project.
Technologies
PythonPyTorchPostgresRedisGoogle Cloud
What it moves
Forecast error against the current baseline
Time from retrain to production
Drift detected before it reaches a decision
Decisions made on leading rather than lagging indicators
Start a Conversation
Have something ambitious in mind?
Tell us what you're building, automating, or transforming.