What types of ML problems do you work on?

Classification, regression, forecasting, anomaly detection, recommendation, and NLP. We match the model type to the problem and your data, not to what's fashionable.

How much data do we need?

It depends on the problem. Some models work with hundreds of labelled examples; others need millions. We'll assess your data during discovery and tell you what's realistic.

Can you use our existing data pipeline?

Yes. We integrate with your existing data infrastructure rather than building a parallel stack — connecting to your data warehouse, streaming pipelines, or feature store.

AI & Data

Machine Learning

Build predictive ML models for forecasting, scoring, and recommendations. Upturn delivers end-to-end machine learning — from data prep to production deployment and ongoing monitoring.

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18%

Average accuracy improvement

6 wks

Model to production

Maintained

Long-term

PredictiveRecommendersScoring

Challenges

The problems we solve for Machine Learning

Decisions driven by gut, not data

Without predictive models, important decisions — inventory, pricing, hiring — rely on intuition that data could significantly sharpen.

Models that never make it to production

Most ML projects succeed in Jupyter notebooks but fail in production due to engineering and operational gaps.

Point-in-time accuracy that degrades

A model trained today is wrong tomorrow as data changes — without maintenance, accuracy erodes silently.

No feedback loop from production data

Deployed models that don't learn from production outcomes miss the richest signal available for improvement.

How we solve it

Our approach to Machine Learning

End-to-end ownership

From notebook to production to maintenance

We build, deploy, and maintain — so you get a managed ML capability, not a one-time project.

Feature engineering and model training
Production API deployment
Scheduled retraining pipelines

Business-calibrated models

Optimized for your objective, not just accuracy

We tune models against the metric that matters to your business, whether that's revenue, risk, churn, or margin.

Business objective alignment
Cost-sensitive evaluation
A/B testing framework

Results

What you can expect

18%

Average accuracy improvement

Uplift over naive baselines documented across forecasting and scoring models.

6 wks

Model to production

Our accelerated path from data to deployed, monitored model.

Maintained

Long-term model health

Retraining pipelines and drift monitoring keep accuracy high over time.

12× ROI

Demand forecasting case

A retail client's demand model returned 12× its development cost in waste reduction.

Methodology

How we deliver Machine Learning

Frame

Define the prediction target, business objective, and success metrics.

Prepare

Clean, engineer, and validate the feature set for training.

Model

Train, evaluate, and compare candidate models rigorously.

Validate

Offline evaluation plus shadow deployment against real traffic.

Deploy

Production inference API with monitoring, versioning, and rollback.

Maintain

Drift detection, scheduled retraining, and monthly accuracy reviews.

What you receive

Feature engineering pipeline
Trained model + evaluation report
Inference API
Retraining pipeline
Model monitoring dashboard
Model card
A/B testing setup

Technology stack

Pythonscikit-learnXGBoostLightGBMPyTorchMLflowFeastFastAPIAWS SageMakerDatabricks

FAQ

Common questions

Ready to explore Machine Learning?

The first consultation is free. Let's find out if this is the right fit for you.

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