Frame the problem
Data feasibility / Baseline models
QUANTEX
Machine learning & predictive systems
Test whether the data supports the decision.
Prediction projects start with data availability, label quality and the cost of being wrong. We establish a baseline before selecting a model and separate training from evaluation data. Deployment planning includes drift, retraining and the operational fallback when a prediction is uncertain or an input changes.
Capabilities and deliverables
- Data feasibility
- Baseline models
- Forecasting
- Classification
- Recommendation systems
- Computer vision
- Feature pipelines
- Model evaluation
- MLOps
- Drift monitoring
A delivery model built around the task
Build the proof
Forecasting / Classification
Validate the release
A reproducible experiment, documented evaluation results and a deployment recommendation tied to the task's error tolerance.
The constellation
Map the decisions, people and information that shape the experience.
Data feasibilityThe connection
Give every hand-off a clear contract, owner and recovery path.
Baseline modelsThe launch
Test the complete journey before it becomes everyday operation.
ForecastingWhat you take away
A reproducible experiment, documented evaluation results and a deployment recommendation tied to the task's error tolerance.
A question worth asking
Is a more complex model always better?
No. Complexity must earn its operating cost through useful performance. A simpler baseline may be easier to explain, maintain and validate.