AI Council · 2026
A framework for measuring the impact of AI products — covering attribution, experimentation, product analytics, and the gap between "usage" and "value."
PyCon Italia · May 2024
Practical patterns for working with large analytical datasets using Pandas v2, Polars, and DuckDB.
Toronto Machine Learning Summit · November 2020
Building a MovieLens recommender system from scratch.
Toronto Womxn in Data Science Conference · March 2020
An overview of hyperparameter tuning methods including grid search, random search, and Bayesian optimization.
PyCon Canada · November 2019
How bias creeps into ML systems through data and design choices, and what practitioners can do about it.
PyCon US · May 2019
A walkthrough of building a diagnostic classification model using clinical data, from feature engineering to evaluation.
PyData DC · November 2018
Hyperparameter optimization techniques applied to medical datasets, covering search strategies and practical trade-offs.
PyData DC · November 2018
A step-by-step tutorial on building collaborative filtering and content-based recommender systems in Python.
PyCon Canada · November 2018
End-to-end design of a recommendation pipeline, from data ingestion to model serving.
PyLadies Vancouver · August 2018
A beginner-friendly overview of how recommendation systems work, covering collaborative filtering and content-based approaches.