Machine Learning & MLOps
Models in production, not in notebooks.
We build machine learning systems that keep working after launch: reproducible training, automated deployment, drift monitoring and clear model documentation.
Sound familiar?
The problems we solve
Pilots that never ship
Promising notebooks that never make it into a business process.
Silent model decay
Models degrade as data changes and nobody notices until results suffer.
Manual, risky releases
Models copied to servers by hand, with no versioning or rollback.
Outcomes
What you get
- Models that move a business metric, measured from the start
- A repeatable path from experiment to production
- Early warning when model performance drifts
Approach
How we deliver it
- 1
Frame the decision
Agree the business metric, baseline and how predictions will be used.
- 2
Build and evaluate
Feature engineering, model selection and honest offline evaluation.
- 3
Productionize
Automated pipelines for training, deployment, monitoring and retraining.
- 4
Measure impact
A/B tests or controlled rollouts against the baseline.
Deliverables
What we hand over
Working software, tests and documentation in your repositories and cloud accounts.
Technologies
- MLflow
- Databricks
- Azure ML
- Amazon SageMaker
- Vertex AI
- scikit-learn
- XGBoost
- PyTorch
- Use-case delivery
- Forecasting, churn, pricing, fraud and recommendation models built end to end.
- Feature store
- Reusable, versioned features shared between training and serving.
- MLOps pipeline
- CI/CD for models with automated testing, approval and rollback.
- Monitoring
- Performance, drift and data-quality monitoring with retraining triggers.
Use cases
Where this makes a difference
Demand forecasting
SKU- and store-level forecasts feeding replenishment and planning.
Fraud and anomaly detection
Real-time scoring of transactions with explainable risk signals.
Churn and next-best-action
Propensity models that drive retention campaigns and service priorities.
FAQ
Common questions
Do we need a data science team first?
No. We can deliver the first use cases and help you hire or upskill as the program grows.
How do you prove a model works?
We agree a baseline and a business metric up front, then measure with controlled rollouts rather than offline accuracy alone.
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Batch and streaming pipelines you can trust
Tell us where your data is today.
A 45-minute conversation with a senior engineer. You leave with an honest view of what to build first, whether or not we work together.