Production ML
Building the infrastructure needed to move models beyond notebooks and into repeatable deployment workflows.
ABOUT
Building machine learning systems that work.
I work across data science, machine learning and software engineering, with a focus on turning models into reliable, usable systems.
BACKGROUND
My focus is increasingly on the engineering layer between a machine learning experiment and a system that can actually be used.
My background combines applied mathematics, computer science and data science. I enjoy problems where statistical thinking, programming and system design intersect.
Rather than treating a trained model as the finished product, I am interested in the infrastructure around it: reproducible training, validation, model lifecycle management, APIs, deployment and monitoring.
19
Distinctions completed during my BSc
ML
Machine learning and data science
MLOps
Production-oriented ML systems
Data systems
Working with structured data, feature pipelines and the interfaces between data preparation and modelling.
Machine learning
Applying statistical and machine learning methods with attention to evaluation, reproducibility and practical constraints.
Applied problems
Interested in using quantitative and computational methods to solve concrete problems where the output has practical value.
EDUCATION
Mathematics meets computer science.
BSC
University of South Africa