Machine Learning Starts Before the Model
In production ML systems, the hardest part is often not the algorithm. It is making data, labels, and evaluation trustworthy enough for the model to matter.
Notes on machine learning and systems.
Writing is where I sharpen how I think about machine learning systems, evaluation, reliability, and technical judgment.
Scientific Reasoning for Applied AI
Scientific training strengthens applied AI because it builds the habits that reliable ML systems depend on: questioning assumptions, isolating variables, and taking evidence seriously.