Machine Learning
That Goes to Production

It depends heavily on the problem type. A simple binary classifier can be effective with a few thousand labelled examples; complex deep learning models may need hundreds of thousands. We always start with a data audit and tell you honestly whether your current dataset is sufficient or whether we need to explore data augmentation, transfer learning, or alternative approaches.

MLOps is the practice of reliably deploying and maintaining ML models in production. Without it, models degrade silently as real-world data drifts from training data. MLOps gives you automated retraining, versioning, monitoring, and rollback capabilities, turning a one-time experiment into a sustainable production system.

Most real-world data is messy. Data cleaning, imputation, and outlier handling are standard parts of our process. We often find that a data engineering investment upfront (building a clean, reliable data pipeline) unlocks significantly better model performance downstream. We'll tell you upfront if your data situation requires more work before modelling is viable.

We set up monitoring dashboards that track prediction distributions, feature drift, and business-level KPIs in real time. Alerts trigger when performance dips below agreed thresholds. You always have visibility into model health, not just accuracy on the original test set, but live performance against real traffic.

Absolutely. We frequently augment internal data science teams, either by taking on specific high-complexity components, providing MLOps infrastructure they don't have bandwidth for, or mentoring junior team members. We can operate as a fully embedded squad or as specialist contractors on defined workstreams.