Writing / Technical
This section brings together writing on applied data science, machine learning, algorithms, AI systems, and the realities of solving problems in professional environments.
I approach this work as a hands-on practitioner and AI leader: someone interested in the design of models and systems, but also in how initiatives are prioritised, how value is created and tracked, and how analytical work translates into decisions that scale across organisations.
The writing is organised across three connected areas. One focuses on machine learning and algorithms, one on LLMs and generative AI, and one on the leadership layer of AI strategy, value, and governance.
Categories
Technical Category
Writing on machine learning, statistical modelling, optimisation, experimentation, and the algorithmic foundations behind real-world analytical systems.
This section focuses on how models are designed, evaluated, and improved in practice, with attention to both technical rigor and the constraints that shape implementation.
Technical Category
Writing on large language models, generative AI systems, prompting, evaluation, use cases, and the practical realities of applying these technologies meaningfully.
The focus here is not only on the technology itself, but on how these systems are applied, assessed, and translated into useful workflows, products, and decisions.
Technical Category
Writing on AI strategy from a hands-on leadership perspective, covering prioritisation, value generation, adoption, governance, risk, and responsible scale.
This section connects technical work to organisational outcomes: how initiatives are shaped, how value is defined and realised, and how AI is governed with clarity and intent.
Orientation
A recurring theme across this section is that technical work matters most when it leads to better decisions, stronger systems, and measurable value in practice.
The goal is to stay grounded in the craft itself: algorithms, modelling, system behaviour, and the realities of implementation, not strategy detached from execution.
The leadership lens here is shaped by technical understanding. It is about knowing what to build, what to prioritise, how to evaluate trade-offs, and how to scale responsibly.