Overview
We develop predictive models and recommendation engines with rigorous experimentation, reproducible pipelines, and automated retraining.
Machine Learning That Works in Production
We develop predictive models and recommendation engines with rigorous experimentation, reproducible pipelines, and automated retraining.
Key Challenges We Solve
- Models that perform in notebooks but fail live
- No versioning for datasets and experiments
- Manual deployment causing downtime
- Bias and fairness issues undetected
Industry Best Practices
Proven approaches built into the Treviox platform.
MLOps from day one
Version datasets, features, and models with CI/CD pipelines for training and deployment.
Experiment tracking
Use MLflow or Weights & Biases to compare experiments and reproduce results.
Feature store discipline
Centralize feature definitions to prevent training-serving skew.
Bias and fairness audits
Evaluate models across demographic slices before production release.
Our Approach
Our ML engineers partner with data teams to establish baselines, then iterate with A/B tests in production.