Machine Learning Development Services

Machine learning development is the engineering of systems that learn patterns from data and make predictions, classifications, or decisions - classification (this email is spam), regression (this property is worth $X), anomaly detection (this transaction is unusual), forecasting (demand next month will be Y), computer vision (this image contains a defect), and natural language processing (this document is a contract). The development lifecycle spans problem framing, data engineering, model training, production deployment, monitoring, and governance - each phase determining whether the model works in a notebook or works in your business.

Five consistent patterns. Poor data quality (#1 - messy, incomplete, or biased data produces unreliable models). Notebook-to-production gap (model works in development, fails in production). No monitoring (model degrades silently as data shifts). Misaligned success metrics (optimising for model accuracy instead of business outcomes). And governance gaps (no explainability, no bias testing, no compliance documentation). None of these are algorithm problems. They're engineering problems - and they're the problems we solve.

If the relationship between inputs and outputs is known, consistent, and expressible as rules - you probably need rules or analytics. If the patterns are too complex for rules, the data is too large for manual analysis, or the relationships change over time - ML adds value. We assess this during discovery and recommend the simplest approach that works. Building ML for a problem that rules would solve wastes months and produces a system harder to maintain.

At minimum: historical data containing both the inputs (features) and the outcome you're trying to predict (the label). Clean, representative, and sufficient in volume - typically thousands of examples for structured data, more for unstructured. Data quality matters more than quantity: 5,000 clean, well-labelled examples outperform 500,000 noisy ones. If your data isn't ready, the first project is often data engineering rather than model development.

An MVP on clean data with a well-defined problem typically takes 8-16 weeks. Add time for data engineering if your data needs preparation (which it usually does). Production deployment, integration, and monitoring infrastructure add additional scope. The most common timeline mistake: underestimating data preparation, which consumes 30-50% of total effort. We share a realistic timeline after the readiness assessment.

LLM Fine-Tuning and RAG are techniques specific to large language models - part of the Generative AI stack. This page covers the full machine learning discipline: classification, regression, anomaly detection, computer vision, NLP, time-series, recommendation systems, and optimisation. XGBoost for churn prediction, CNNs for quality inspection, time-series models for demand forecasting - the ML techniques that don't involve generating text. Some projects use both: an ML model for prediction and an LLM for explanation.

MLOps infrastructure: continuous monitoring of prediction distributions and feature values, automated data drift detection that alerts when the input data changes, concept drift detection that identifies when the relationship between inputs and outputs shifts, and automated retraining pipelines that refresh the model using recent data. Fewer than 40% of deployed models sustain value beyond 12 months - almost always because monitoring was absent. We build it as infrastructure, not as an afterthought.

Yes - and in regulated industries, they must. SHAP and LIME provide feature importance and individual prediction explanations for most model types. Simpler models (linear, tree-based) are inherently interpretable. Complex models (deep learning) require post-hoc explainability techniques. We select model architectures that balance accuracy with the explainability your domain requires - because a model your compliance team can't explain is a model that can't deploy.

ML models can amplify biases present in training data - and in domains like hiring, lending, insurance, and healthcare, biased predictions create legal liability. We test for bias during data preparation (is the training data representative?), during model evaluation (does the model perform equally across demographic groups?), and in production (do prediction patterns show disparate impact?). Fairlearn and AIF360 for measurement. EU AI Act high-risk provisions for compliance. Bias testing is not a checkbox - it's a continuous engineering discipline.

Cost varies by problem complexity, data preparation effort, model type, and production infrastructure requirements. A classification model on clean tabular data is a different scope from a computer vision system processing video. Key cost drivers: data engineering effort (the largest variable), model complexity, integration depth, deployment model (cloud vs edge), and monitoring/governance requirements. We scope fixed pricing after the readiness assessment, because the data preparation effort - which varies enormously - is the largest cost component.