Sophie Watson
where else to find them
no profile links on file
what they talk about
all 9 in the filters
where they keep going back
- Berlin Buzzwords5 editions2018–2023
all 4 in the filters
on a stage with
- William Benton3×
- Rui Vieiraonce
who talks about the same things
- William Benton15 talks
- Salman Iqbal11 talks
- Ed Shee5 talks
- Animesh Singh23 talks
- Didier Girard13 talks
- Yuzhui Liu6 talks
| title | conference | date |
|---|---|---|
| Avoiding Anti-patterns in Technical Communication | Berlin Buzzwords 2023 | 2023-06-18 |
Lists four concrete anti-patterns: keeping it brief (example: “Notebook won’t run” Slack messages that force extra triage), chasing likes (99% accuracy… | ||
| Luxuries, necessities, and the challenges that remain | Berlin Buzzwords 2022 | 2022-06-12 |
Shows concrete results using Nvidia hardware and Python: cuPy and RAPIDS let UMAP run at interactive latency so visual exploration uses full data instead of… | ||
| Facets of Reproducible research on Kubernetes | Berlin Buzzwords 2021 | 2021-06-15 |
Kubernetes plus CI/CD and container images make environments reproducible, but real reproducibility requires concrete steps: use binder or declarative images… | ||
| Cloud Native Machine Learning Systems at Day Two and Beyond | KubeCon + CloudNativeCon North America 2020 Virtual | 2020-11-17 |
Kubernetes becomes the default infra for ML but it leaves gaps around reproducibility, interactive velocity, and model monitoring; expect to use CI/CD (Tekton)… | ||
| DevOps for Data Science With Kubernetes | MLconf Online 2020 | 2020-11-05 |
Demonstrates a pipeline that takes Git-hosted Jupyter notebooks, uses a source-to-image builder to extract dependencies, run training and produce a… | ||
| Dealing with pain points of recommenders in the real world | Berlin Buzzwords 2019 | 2019-06-16 |
Alternating least squares shows practical gaps: it handles explicit or implicit data but not both, is sensitive to anomalies, and ignores temporal drift. The… | ||
| Recommendation engines explained | DevConf.CZ 2019 | 2019-01-25 |
Uses the MovieLens 100k dataset and Jupyter/Binder to demonstrate Spark ALS collaborative filtering, trains with a grid over rank {6,8} and maxIter {10,12}… | ||
| Learning "Learning to Rank" | DevConf.CZ 2019 | 2019-01-25 |
Covers how to turn query–document pairs into numeric feature vectors (IDF, term-in-title, link counts), the need for human relevance labels (0–4), and… | ||
| What they didn’t teach you in Grad School #bbuzz | Berlin Buzzwords 2018 | 2018-06-11 |
Implements a well‑cited ALS recommender (4416 citations) on the 26 million ratings MovieLens set using an off‑the‑shelf Apache Spark implementation, and finds… | ||
| nothing matches | ||