Applied AI
Machine Learning
Overview
Most of the work in machine learning happens after the model is trained. We engineer the pipelines, monitoring and integration that let models change decisions for real.
Outcomes we measure
- Model lift against the current baseline
- Time from experiment to production
- Prediction latency and availability
- Drift detected before it hurts decisions
Problems we solve
Sound familiar?
- 01
Data scientists build promising models that never reach production.
- 02
A model in production degraded for months before anyone noticed.
- 03
The business does not trust predictions it cannot explain.
What we deliver
Concrete deliverables, not slideware.
Use-case framing
Decision, baseline, success metric and cost of error agreed before modelling starts.
Models
Forecasting, classification, ranking and anomaly detection — the simplest model that works.
MLOps
Feature pipelines, model registry, CI/CD, batch and real-time serving.
Monitoring and explainability
Drift, performance and fairness monitoring, with explanations for reviewers.
How it works
From first call to production.
- 01
Frame
Define the decision, the baseline and how success will be measured.
1–2 weeks
- 02
Experiment
Feature engineering and model selection against the baseline.
3–6 weeks
- 03
Productionise
Pipelines, serving, monitoring and integration into the workflow.
4–8 weeks
- 04
Monitor and retrain
Drift alerts, scheduled retraining and periodic model reviews.
Ongoing
Tech & frameworks
Tools we use — in your stack.
- Python
- scikit-learn
- XGBoost
- PyTorch
- MLflow
- Databricks ML
- SageMaker
- Vertex AI
- Azure ML
- Feast
Related projects
Where we've done this.
Case studies
Case studies for this service are being prepared. Ask us for references on a call.
FAQ
Frequently asked questions
Applied AI
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Scope it with a senior practitioner
Book a 45-minute readiness call.
Where you are, what to fix first, and what it would take.