Andy Terrel
where else to find them
no profile links on file
what they talk about
all 7 in the filters
where they keep going back
- PyData London2 editions2024–2025
- PyData NYC2 editions2023–2024
all 7 in the filters
on a stage with
who talks about the same things
- Katrina Riehl6 talks
- Jacob Tomlinson10 talks
- Travis E. Oliphant13 talks
- Peter Wang17 talks
- Sponsor1 talk
- Ian Ozsvald17 talks
| title | conference | date |
|---|---|---|
| Building Inference Workflows with Tile Languages | PyData Seattle 2025 | 2025-11-07 |
Claim: inference gets 15x lower cost per token on recent Blackwell hardware and a tile compiler maps NumPy-style array code to tensor cores. Tools actually… | ||
| CUDA in Python- A New Era for GPU Acceleration | PyData London 2025 | 2025-06-06 |
Concrete claims and tools: Python bindings for CUDA (nvmath-python, cccl-python, cuda-core, numba-cuda) plus cuPy, cuDF and cuPy‑numeric/Legate stack that… | ||
| Accelerating GPU Algorithms in pure Python | PyData NYC 2024 | 2024-11-06 |
Claims that pure Python kernels JIT‑compile to the same code and match CUDA C++ performance, demonstrated with a copy, tiled matrix transpose, and batched… | ||
| GPU development in Python 101 | PyData London 2024 | 2024-06-14 |
Claims: Python can write GPU code no C++ required and run it on Colab T4s. Tools actually used: Numba kernels, cuPy, cuDF (the cuDF pandas plugin), Dask with… | ||
| The Beauty and the Beast: Python on GPUS | PyData NYC 2023 | 2023-11-01 |
Python code runs correctly on GPUs only when it handles memory and threading explicitly or uses tooling that does it for you: low‑level CUDA and CUDA Python… | ||
| How to review a model | PyData Global 2020 | 2020-11-11 |
Models are framed as a high‑risk technical‑debt asset and the claim is that disciplined review practices pay that debt down. The repo contains notebooks, a… | ||
| Becoming a Data Head, a reflection on the trade offs and decision | PyData Austin 2019 | 2019-12-07 |
Claims concrete tradeoffs: builds mission-driven hiring to recruit people, runs a platform data team not point-to-point, and accepts that ~50% project failure… | ||
| Building an Open Platform for Sustaining Data Science Innovation | PyData Los Angeles 2018 | 2018-10-17 |
NumFOCUS claims an operational platform: 26 sponsored and 24 affiliated projects producing roughly 50 packages that combine into a reusable stack. Example… | ||
| Dev Ops meets Data Science Taking models from prototype to production with Docker | PyData DC 2016 | 2016-10-07 |
Shows a working path from prototype model to production API using Docker, Kubernetes and Anaconda and reports concrete outcomes: Docker image deploy in 10… | ||
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