Machine Learning Development
Predictive models, classification, and computer vision built with proper validation, monitoring, and retraining pipelines.
- Baseline
- Simple models first
- Monitored
- Drift detection built in
- 6-12 wk
- Typical build
About machine learning development
Classical machine learning remains the right tool for a great many problems — forecasting, churn prediction, fraud detection, quality inspection — where you have structured historical data and a well-defined target.
Validation that reflects reality
The most common failure in applied ML is a model that performs well in testing and poorly in production, usually from data leakage or a validation split that does not reflect how the model will be used. We validate against time-based splits and hold-out sets constructed to mirror production conditions.
Baselines before complexity
We start with a simple baseline. Frequently a well-tuned gradient boosting model on good features outperforms something more elaborate, trains in minutes, and is far easier to explain to stakeholders and regulators.
Monitoring for drift
Models degrade as the world changes. We deploy with monitoring on input distributions and prediction quality, plus a retraining pipeline, so decay is detected rather than discovered through a business problem.
What the engagement includes
Feature engineering
Domain-informed features built with your subject matter experts.
Model development & validation
Time-aware splits and hold-outs reflecting production use.
Computer vision
Image classification, detection, and quality inspection where applicable.
Deployment pipelines
Reproducible training and serving, not notebooks in production.
Drift monitoring & retraining
Input and prediction monitoring with retraining triggers.
What you get
The measurable results this service is accountable for.
- Validation designed to catch leakage and overfitting
- Simple, explainable baselines before complexity
- Deployment pipelines rather than notebooks
- Drift monitoring with retraining triggers
- Explainability for stakeholders and regulators
A process without surprises
Clear checkpoints at every stage, so you always know what is shipping and when.
- 1
Discovery & feasibility
We assess the use case, data readiness, and whether AI is genuinely the right tool before proposing a build.
- 2
Prototype & evaluation
A working prototype measured against defined accuracy and cost criteria, so the decision to proceed is evidence-based.
- 3
Production build
Hardening, guardrails, monitoring, evaluation harness, and integration with your systems.
- 4
Monitor & improve
Ongoing quality monitoring, prompt and retrieval tuning, and model updates as the landscape changes.
What is included at each tier
Engagements scale with your stage. Every tier includes everything below it.
| What's included | Starter | Growth | Enterprise |
|---|---|---|---|
| Data & feasibility assessment | Included | Included | Included |
| Baseline model & validation | Included | Included | Included |
| Production model & pipeline | Not included | Included | Included |
| Deployment & serving | Not included | Included | Included |
| Drift monitoring & retraining | Not included | Not included | Included |
Sectors we work in
We validate against production-like conditions and deploy with drift monitoring, so models keep working after launch.
Common questions
How much data do we need?
Can you explain model decisions?
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Ready to talk about machine learning development?
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