(unnamed presenter)
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what they talk about
all 5 in the filters
where they keep going back
no series more than once — all 2 in the filters
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who talks about the same things
- Dean Wampler29 talks
- Jules S. Damji7 talks
- Sponsor1 talk
- Avik Basu8 talks
- Vincent D Warmerdam22 talks
- Robert Nishihara11 talks
| title | conference | date |
|---|---|---|
| Skrub: machine learning for dataframes | PyData Paris 2025 | 2025-09-30 |
Skrub claims to convert Pandas/Polars tables into scikit‑learn ready feature matrices with minimal code and reproducible operations; it supplies concrete… | ||
| Distributed Python with Ray-Hands on with the Ray 2.0 APIs for scaling Python Workloads | PyData NYC 2022 | 2022-11-09 |
Hands-on demos use Ray 2.0 Core APIs and Ray AIR to scale Python from laptop to cluster: remote functions as tasks, remote objects as futures, and stateful… | ||
| ML Latency No More- Useful Patterns to Reduce ML Prediction Latency to Sub X ms | PyData NYC 2022 | 2022-11-09 |
Argues that latency is a business constraint not a nicety: treat ~100 ms as the cutoff (examples use <10 ms timeouts), measure optimizing vs satisficing… | ||
| NixtlaVerse, bridging the gap between statistics and deep learning for time series | PyData NYC 2022 | 2022-11-09 |
NixtlaVerse delivers concrete tooling and numbers: StatsForecast (PyTorch + NumBa) runs AutoARIMA/ETS at scale — claim is 10 million series in under 43 minutes… | ||
| nothing matches | ||