Some thoughts on machine learning

This year, I wrote some posts for the Openlayer blog. I've broadly categorized them below. If you find something interesting, let me know!

Model evaluation and error analysis

• The Openlayer white paper (back then, Openlayer was still called Unbox.)

• Testing and its many guises.

• Model evaluation in machine learning.

• Show me your ML development pipeline and I'll tell you who you are.

• Debugging models with the bias-variance trade-off.

Explainability

• Understanding Shapley values.

• Understanding LIME in 5 steps.

• The roads toward explainability.

Classic machine learning concepts

• The representation of meaning.

• Dealing with class imbalance - Part 1.

• Dealing with class imbalance - Part 2.

• Ensemble learning 101.

• Baseline models.

Machine learning in production

• Mental models for ML products.

• Why data-centric AI.

• 3 differences between ML in production and in academia.