Knowledge Hub

The Executive Guide to AI Strategy

A practical framework for defining your AI roadmap, prioritising use cases, and building the governance structures that keep initiatives on track. Written for C-suite and senior leaders.

Data Governance in the Age of AI

How to build data governance frameworks that enable AI without creating bureaucratic bottlenecks. Covers ownership models, quality standards, and compliance requirements for ML-ready data.

MLOps Maturity Model

A five-level maturity model for ML operations — from ad-hoc notebooks to fully automated retraining and deployment pipelines. Assess where you are and map the path to the next level.

Building a Modern Lakehouse: A Practical Guide

Step-by-step guidance for designing and implementing a medallion architecture data platform. Covers tool selection, partition strategy, schema evolution, and data quality frameworks.

Feature Engineering for Production ML

How to build feature pipelines that work in both training and serving environments. Covers feature stores, point-in-time correctness, and avoiding training-serving skew.

Designing a Metrics Store Your Business Will Actually Use

A practical guide to building a semantic layer — defining metrics, establishing ownership, and creating a single source of truth that survives organisational change.

Getting Started with dbt for Data Transformation

A hands-on tutorial covering dbt fundamentals: project structure, models, tests, and documentation. By the end you will have a working transformation pipeline on a sample dataset.

Your First Production ML Model in Python

Build, evaluate, and deploy a classification model end-to-end. Covers data preparation, model selection, evaluation, containerisation, and a simple serving endpoint.

Real-Time Data Pipelines with Kafka and Python

Learn to produce and consume Kafka events, implement exactly-once semantics, and build a simple stream processing job that writes to a data lake.

AI Foundations for Business Leaders

A structured six-module learning path covering AI concepts, use case identification, vendor evaluation, and the organisational changes required to make AI work. Designed for non-technical leaders.

Modern Data Engineering

Eight modules taking you from data pipeline fundamentals through to lakehouse architecture, streaming systems, and data quality engineering. Suitable for engineers transitioning into data roles.