AI & ML · Applied ML
Models that earn their production seat.
CRISP-DM aligned ML delivery — from business problem framing to production models on tabular, text and vision data.
Applied machine learning is a delivery discipline, not a science fair. We follow the CRISP-DM lifecycle — business understanding, data understanding, preparation, modelling, evaluation, deployment — with clear success criteria and offline / online evaluation gates before anything reaches users.
/ Capabilities
What's in scope.
Problem framing
Translate business questions into ML problems with measurable objective functions, labels and success criteria.
Feature engineering
Reproducible feature pipelines with a shared feature store where reuse across models justifies the investment.
Modelling
Classical ML (XGBoost, LightGBM), deep learning for text/vision, and forecasting — chosen for fit, not novelty.
Evaluation & monitoring
Rigorous offline evaluation, A/B and interleaved online testing, and drift monitoring in production.
Simple models first.
A well-instrumented logistic regression beats a poorly-shipped deep model. We baseline with simple, explainable approaches and add complexity only where it moves the business metric.
Evaluation is a system, not a step.
We build evaluation pipelines that run on every training job, with slice-based metrics and fairness checks — so regressions are caught in CI, not in production.
/ Standards & Tooling
Common questions.
Do we always need deep learning?
No. For tabular problems, gradient-boosted trees remain state of the art and are cheaper to train, deploy and monitor. Reserve deep learning for text, vision, audio and problems where representation learning helps.
How do we know the model is working in production?
Through paired offline evaluation and online experimentation (A/B, holdouts) plus continuous drift monitoring — and by tying a model's KPI to a business KPI it demonstrably moves.
Ready to scope a applied machine learning engagement?
A senior practitioner — not a sales rep — will respond within one business day.
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