Building a Private MLOps Platform on AWS
·3 mins
How I deployed MLflow as a authenticated experiment tracking server on AWS and integrated it into a reusable ML toolkit.
Machine-Learning
Using Learning Curves to Know Whether More Data Will Help
Learning curves won’t tell you exactly how many samples to collect, but they will tell you whether collecting more is worth it at all. In domains where each sample costs real money, that’s the question that actually matters.
Building a Reusable ML Toolkit for Genomic Models
A Python package with a YAML-driven pipeline builder and a prediction CLI.
When Random Features Work Just as Well
On the counterintuitive finding that randomly selecting features from high-dimensional genomic data often matches the performance of careful feature engineering and why that makes mathematical sense.
Scaling ML Training for Epigenetic Age Prediction
How parallelising hyperparameter tuning on SageMaker turned a single-instance grid search into a 100x faster training workflow.