Gael Varoquaux
where else to find them
website:https://gael-varoquaux.info
github:GaelVaroquaux
bluesky:gaelvaroquaux.bsky.social
speakerdeck:gaelvaroquaux
what they talk about
where they keep going back
- dotAI2 editions2024–2025
all 4 in the filters
on a stage with
never shared a talk
who talks about the same things
- Vincent D Warmerdam22 talks
- Kevin Lemagnen3 talks
- Ramon Perez8 talks
- (unnamed presenter)4 talks
- Paco Nathan8 talks
- Ian Ozsvald17 talks
| title | conference | date |
|---|---|---|
| Skrub DataOps, Probabl | dotAI 2025 | 2025-11-06 |
Claims that messy data science code yields 88% of projects failing to reach production and that the fix is better primitives, not new models. Demonstrates… | ||
| Skrub: Less data wrangling, more machine learning | dotAI 2024 | 2024-10-17 |
Claims that Skrub slashes the 80% data‑prep burden by providing a Python library that converts messy tables into model‑ready features, shipping a table… | ||
| Dirty Data Science Machine Learning On Non Curated Data | PyData Global 2020 | 2020-11-11 |
Claims that string‑aware encodings and simple pipelines let supervised models learn directly from messy tables without manual curation, demonstrating a dataset… | ||
| Keynote: Easier Machine Learning Thoughts From Scikit-Learn | Ray Summit 2020 | 2020-09-30 |
Scikit-learn focuses on concrete wins: algorithmic changes that speed real workloads, not vague scaling promises. Examples include randomized PCA (≈10x speedup… | ||
| Understanding and diagnosing your machine-learning models | PyData London 2018 | 2018-04-26 |
Claims concrete diagnostics not platitudes: uses scikit-learn metrics (explained variance, mean absolute error), cross_val_score, DummyClassifier and… | ||
| nothing matches | ||