Hi, I’m Greg 

I’m a Data Scientist with over a decade of experience building ML systems in industry and a PhD in Applied Statistics from the University of Cape Town, South Africa. Find out more about my journey from academia to industry here.

I created this space to share structured thinking on how AI, Data Science (DS), and Machine Learning can drive decisions and value in organisations -- technically, operationally, and strategically. These articles are based on real-world experience designing, deploying, and scaling ML systems inside organisations. 

This site is aimed at Data Scientists, AI & ML Engineers, Product Managers, and Technical Leaders working with applied ML. 

We're living in a world where AI can generate code, explain concepts, and summarise papers in mere seconds. The scarce resource is no longer information. Rather, what remains scarce, is judgement; knowing which approach to trust, which to optimise for, and what trade offs matter and why. 

My goal is to add judgement where information is abundant -- to bridge theory, implementation, and commercial interests, so that teams can build and deploy AI & ML systems using best practice. 

I'm always interested in hearing how others are applying AI/ML -- the successes, and learnings. Feel free to get in touch, or to subscribe to get notified when I publish a long-form article on this site. 

What this site is

This is a collection of lessons, notes, reflections, and structured thinking on applied ML, DS, and AI. My interests are broad, but, will typically converge on one of the following focus areas:

Model Development: The goal of any model is to create value for an organisation. A key focus of this work is understanding how to build models that are well-aligned to the problem at hand and perform effectively in practice. These articles explore the full modelling process, from problem framing and data exploration through to model selection, evaluation, and iteration.

Building & Scaling ML Systems: Building models in POCs or small-scale environments is often straightforward. The real challenge is ensuring they work reliably in production. This area focuses on how to design and scale ML systems effectively, and how to navigate the trade-offs that emerge when moving from prototypes to production systems.

Data, AI & ML Strategy: Many organisations aim to become more data-driven, but there is often a gap between current capabilities and that ambition. This area explores how organisations can bridge that gap through practical, incremental strategic decisions across short-, medium-, and long-term horizons. 

LLMs and Gen-AI: Large Language Models are powerful, but not universally applicable. Their architecture makes them well-suited to some use cases and less effective in others. This section focuses on where LLMs create real value, where they do not, and how to make informed decisions about their use in practice.

My Philosophy

I approach AI & ML like a scientist: forming hypotheses, testing assumptions, and interpreting outcomes objectively, even when data is messy or organisational processes are inconsistent. 

This lens guides everything I write, from project reflections, technical implementations, to critiques of emerging trends.