Solution 04 / Machine Learning Systems

Models that keep working after the launch demo.

Feature pipelines, deployment and monitoring for ML systems that must stay accurate as the world they were trained on moves.

Discuss ML Systems ↗

01The problem

A model that validates well offline and degrades in production is usually not a modelling failure. It is training/serving skew: features computed one way in the training notebook and another way in the serving path. Add no drift monitoring and no retraining trigger, and accuracy decays invisibly until someone downstream notices the business metric moved.

Typical symptoms

Offline metrics that never reproduce in production
Features computed twice: once in training, once in serving, in different code
No answer to "which model version produced this prediction, from which data?"
Retraining that happens when someone remembers, not when the data says to
Model performance discussed anecdotally because nothing measures it live
Rollback that means redeploying an old branch and hoping

Decisions we help you make

Real-time versus batch scoring, decided by how fresh the decision actually needs to be
Whether a feature store is justified, or disciplined shared transformation code is enough
What triggers retraining: schedule, drift threshold, or performance floor
How new models are validated in production: shadow, canary, or champion/challenger
What is logged at inference time to make any prediction reconstructable later
Where the accuracy/latency/cost trade-off should sit for this specific decision

02How we work on it

Methods we apply.

Single-definition feature pipelines shared by training and serving, closing skew by construction

Experiment tracking and a model registry with reproducible training runs

Shadow deployment and champion/challenger evaluation on live traffic

Data drift, concept drift and prediction-distribution monitoring with actionable thresholds

Automated retraining pipelines with validation gates before promotion

Inference logging designed for auditability and post-hoc analysis

CI/CD for models: versioned data, code, and artefacts, with tested rollback

Offline accuracy that never reproduces in production is usually this: a feature computed one way for training and another way at serving time. The fix is structural: one definition, used by both paths.

03What moves

Metrics this work is measured on.

Live model performance vs. offline baselineTraining/serving skew incidentsFeature and prediction driftTime from retrain trigger to deployed modelPrediction latency (p95)Rollback time

04What we need to start

Existing models, training code, and how they are deployed today
Feature definitions and where they are computed
Historical training data and current production inputs
The business metric the model is meant to move
Any audit, explainability or regulatory requirements

05Engagement path

How this becomes an engagement.

ML Systems Review

2 weeks

Audit the path from data to prediction and identify where accuracy leaks between training and production.

MLOps Build

2–4 months

Implement feature pipelines, registry, deployment strategy and drift monitoring.

Monitoring Handover

Ongoing

Your team owns retraining and monitoring, with the thresholds and runbooks already tuned.

Want to see what this looks like against your own systems?

Most engagements start with a short, fixed-scope assessment, enough to quantify the opportunity before anyone commits to a build.

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