AI & Machine Learning
ML pipeline development, MLOps platform engineering, and GenAI strategy for UK enterprises. We build AI systems on production-grade data foundations.
ML Pipeline Development
Feature Engineering Pipelines
PySpark data engineering for feature preparation at scale. Feature stores on Databricks Feature Engineering or Feast. Automated feature pipelines that feed both training and inference.
Model Training Infrastructure
Distributed training on Databricks ML clusters, hyperparameter tuning with Hyperopt, and experiment tracking with MLflow. We design training pipelines that are reproducible and audit-ready.
Batch Inference Pipelines
Scheduled scoring pipelines using Databricks Workflows or Dagster. Output written to Delta tables or Snowflake for downstream consumption by reporting and applications.
Real-time Inference
Model serving via Databricks Model Serving, Azure ML Online Endpoints, or custom FastAPI deployments. Low-latency scoring for applications requiring sub-second response times.
MLOps Platform Engineering
MLOps consulting UK — we build the infrastructure that takes models from notebooks to production and keeps them performing over time.
MLflow Integration
Experiment tracking, model registry, and deployment workflows using MLflow on Databricks or self-hosted.
Model Monitoring
Drift detection, data quality monitoring, and performance degradation alerts. We build monitoring that catches model failures before the business does.
CI/CD for ML
Automated testing for ML models — unit tests for feature logic, integration tests for pipeline outputs, and canary deployments for model updates.
Databricks ML
Databricks Machine Learning tier — Unity Catalog model governance, Feature Engineering, AutoML, and Model Serving in a unified platform.
Azure ML
Azure Machine Learning workspaces, compute clusters, designer pipelines, and Managed Online Endpoints for enterprise Azure deployments.
Model Governance
Model cards, approval workflows, bias assessment, and audit trails. Responsible AI frameworks aligned with UK regulatory guidance.
GenAI Strategy & Implementation
We help organisations navigate the GenAI landscape — from strategy and use-case prioritisation through to production RAG architectures.
GenAI Use Case Assessment
Structured evaluation of where GenAI adds genuine value in your organisation. We filter hype from substance and prioritise use cases by ROI and feasibility.
RAG Architecture Design
Retrieval-Augmented Generation architectures on Databricks Vector Search, Azure AI Search, or pgvector. We design RAG pipelines that give LLMs accurate, up-to-date context from your enterprise data.
LLM Integration
OpenAI, Azure OpenAI, and open-source LLM integration into data pipelines and applications. Prompt engineering, output parsing, and structured generation.
Responsible AI
Evaluation frameworks, bias testing, and guardrails for GenAI systems in regulated industries. We build AI systems that your legal and compliance teams can approve.
Data Engineering is the Foundation
Machine learning and AI systems are only as good as the data they run on. PySpark data engineering for feature preparation is inseparable from ML delivery. Every AI/ML engagement we run starts with an assessment of data quality, availability, and infrastructure readiness.
Learn about our Data Engineering ServicesReady to discuss your AI or ML platform needs? Contact Us