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Artificial Beingz
Data Science & Machine Learning

Models for forecasting, risk, vision and decision-making, built on your data, measured against a baseline and monitored after they ship.

01

Predictive Modeling

Every model starts against a simple baseline. If it can't beat the baseline clearly, it doesn't ship.

Typical problems:

  • Default and prepayment risk in lending
  • Tenant screening and arrears prediction
  • Churn, pricing and propensity models
  • Explainable outputs, such as per-applicant reason codes for lending decisions
python
1model = xgb.XGBClassifier(
2 max_depth=5, n_estimators=600, learning_rate=0.03,
3 early_stopping_rounds=50,
4)
5model.fit(X_train, y_train, eval_set=[(X_val, y_val)])
6
7# Reason codes for each applicant, not just a score
8explainer = shap.TreeExplainer(model)
9reasons = explainer.shap_values(X_applicant)

02

Forecasting & Anomaly Detection

Forecasts that account for trend and seasonality and come with honest uncertainty ranges, plus alerts for anything that doesn't fit the pattern.

Applications:

  • Occupancy, demand and cash-flow forecasting
  • Anomaly detection on transactions and payments
  • Sensor and telemetry monitoring for predictive maintenance

03

Computer Vision

Classification, object detection and segmentation for problems where the data is images or video.

Where we've applied it:

  • Sports analytics: tracking player movement and game events
  • Healthcare: medical image analysis and annotation
  • Document and property imagery

04

MLOps

A model that works in a notebook isn't finished. We put models into production and keep them healthy after launch.

Setup:

  • MLflow experiment tracking and model registry
  • Feature pipelines shared between training and serving
  • Drift and performance monitoring with alerts
  • Scheduled retraining with an approval step before promotion
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LOCATION

4025 River Mill Way,
Mississauga, L4W4C1
ON, Canada

GET IN TOUCH

contact@artificialbeingz.com

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