## Improving a Machine Learning System (Part 3 - A/B testing)

This post is part three in a three part series on the challenges of improving a production machine learning system. Find part one here and part two here. A/B Testing When engineers and data scientists optimize machine learning systems they often focus on improving offline metrics like cross entropy, ROC-AUC,... [Read More]
Tags: Machine Learning, Machine Learning Systems, A/B, A/B testing

## Improving a Machine Learning System (Part 2 - Features)

This post is part two in a three part series on the challenges of improving a production machine learning system. Find part one here and part three here. Adding New Features or Improving Existing Features A machine learning model is only as powerful as the features it is trained with.... [Read More]
Tags: Machine Learning, Machine Learning Systems, Features

## Optimizers as Dynamical Systems

The ideas in this post were hashed out during a series of discussions between myself and Bruno Gavranović Consider a system for forecasting a time series in $$\mathbb{R}$$ based on a vector of features in $$\mathbb{R}^a$$. At each time $$t$$ this system will use the state of the world (represented... [Read More]
Tags: Machine Learning, Category Theory, Lens, Dynamical System, Gradient Descent

## Supervised Clustering With Kan Extensions

Clustering algorithms allow us to group points in a dataset together based on some notion of similarity between them. Formally, we can consider a clustering algorithm as mapping a metric space $$(X, d_X)$$ (representing data) to a partitioning of $$X$$. In most applications of clustering the points in the metric... [Read More]
Tags: Clustering, Machine Learning, Extrapolation, Kan Extension, Category Theory, Functorial