Data Science & ML
Machine Learning and LLM Fine-Tuning, Built for Production
We take data science from notebook to production — engineering features, training and evaluating models, fine-tuning language models on your domain data, and operating them with the MLOps discipline that keeps results trustworthy.
Reference architecture
How the pieces connect
A reference architecture we adapt to each organisation's existing platforms, constraints and goals.
Capabilities
What we deliver in Data Science & ML
Data Science
Exploratory analysis, statistical modelling and experiments that turn business questions into measurable answers.
Machine Learning
Classification, regression, forecasting, ranking, recommendation and anomaly-detection models.
Feature Engineering
Reusable, governed features served consistently for both training and inference.
ML Model Training
Reproducible training pipelines with hyperparameter tuning and distributed training on Spark and GPUs.
Deep Learning
Neural networks for text, time-series and tabular problems where simpler models fall short.
LLM Fine-Tuning
Supervised and parameter-efficient fine-tuning (LoRA, QLoRA) to adapt open or commercial models to your domain.
Training Data Curation
Curated, governed training sets and held-out evaluation sets built from your own data.
Model Evaluation
Offline metrics, bias and robustness checks, and LLM evaluation that prove a model is better before release.
MLOps
Experiment tracking, model registry, CI/CD and automated retraining for models in production.
Model Deployment & Serving
Batch, real-time and streaming inference with versioning, scaling and safe rollback.
Model Monitoring
Drift, data-quality and performance monitoring with alerts and retraining triggers.
Responsible ML
Explainability, fairness testing, lineage and audit trails for regulated environments.
Use cases
Where it makes a difference
Predictive models
Forecasting, churn, propensity, risk and demand models trained on governed lakehouse data.
Domain-adapted language models
Fine-tune a language model on your terminology and tasks, evaluated against a held-out test set.
MLOps platform
Standardise experiment tracking, registry, deployment and monitoring across data science teams.
Explore more
Related solutions
Data & Cloud
Modern, governed data platforms on the cloud — engineered for scale, reliability and AI readiness.
AI & GenAI
Generative AI, RAG and agentic systems engineered for enterprise reliability, security and control.
Analytics & Intelligence
From business intelligence to decision intelligence — analytics that changes what people do next.
AdTech & MarTech
Customer data platforms, identity resolution and activation that turn data into personalised experiences.
FAQ
Data Science & ML: common questions
What is LLM fine-tuning and when is it worth doing?
LLM fine-tuning continues training a pre-trained language model on your own examples so it learns your terminology, format or task. It is worth it when prompting and retrieval are not enough, such as for consistent style, specialised classification or lower-latency smaller models. We always measure gains against a held-out evaluation set first.
Should we fine-tune a model or use RAG?
They solve different problems. RAG supplies up-to-date facts from your documents at question time; fine-tuning changes how the model behaves, writes or classifies. Many production systems combine both. We start with the simplest approach that meets the quality target and add fine-tuning only where evaluation shows a clear benefit.
What do you need to train a machine learning model?
A clearly defined prediction target, enough representative historical data, a way to measure success in business terms and a path to deploy the result. We help assess data readiness, build features and training pipelines, evaluate models against baselines, and deploy them with monitoring.
What is MLOps?
MLOps applies engineering discipline to machine learning: versioned data and code, experiment tracking, a model registry, automated testing and deployment, and monitoring for drift and quality, with retraining when performance drops. It keeps models reproducible, auditable and reliable after the first release.
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