Rebecca Bilbro
where else to find them
what they talk about
all 11 in the filters
where they keep going back
- PyData London2 editions2017–2024
- PyData DC2 editions2016–2018
all 9 in the filters
on a stage with
all 4 in the filters
who talks about the same things
- Patrick Deziel4 talks
- Vincent D Warmerdam22 talks
- Avik Basu8 talks
- Ramon Perez8 talks
- Kevin Lemagnen3 talks
- Eduardo Blancas4 talks
| title | conference | date |
|---|---|---|
| Where Have All the Metrics Gone | PyData Boston 2025 | 2025-12-08 |
Explains how RAG systems and LLM agents fail in concrete ways and how to measure each failure: domain failure (authoritative but subtly wrong, only catchable… | ||
| Data Secrets from a Platform Engineer | PyData NYC 2024 | 2024-11-06 |
Claim that rewriting ORM queries to raw SQL cuts runtime from 8 hours to minutes; note warehouses lack DB guarantees like global IDs so entity‑resolution… | ||
| Mistakes were made: Data science ten years in | PyData London 2024 | 2024-06-14 |
Claims a low deployment success rate and lists concrete anti‑patterns: building diagnostics like Yellowbrick, chasing t-SNE vs UMAP visual signal, relying on… | ||
| A Witch’s Guide to Go: 3 Charms to Enchant Your Software | GopherCon 2023 | 2023-09-26 |
Describes building Enen, a streaming event store with a custom Raft-replicated SQL-like DB that exposes deep behavior through a one-line Connect(DSN, readOnly)… | ||
| How To Build a Distributed System (And Should You?) | KubeCon + CloudNativeCon North America 2022 | 2022-10-24 |
A geo-distributed global directory service is built and deployed to clusters in the US, Germany, and Singapore using gRPC, Kubernetes, LevelDB and Prometheus… | ||
| Data Structures for Data Privacy: Lessons Learned in Production | PEPR '22 | 2022-06-23 |
Decentralized protocol implements a peer‑to‑peer PKI and a global directory yellow pages to mutually authenticate VASPs, uses gRPC/protobuf plus mTLS, and… | ||
| PyTesting the Limits of Machine Learning | PyCon US 2021 | 2021-05-14 |
Shows concrete techniques: use scikit‑learn Estimator/Transformer, pipelines and FeatureUnion to reduce test surface; fix random_state to make runs repeatable… | ||
| Words in Space | PyData Miami 2019 | 2019-01-09 |
Visual diagnostics show specific, actionable differences: using Yellowbrick and scikit‑learn with SciPy metrics (Euclidean, Manhattan, Minkowski, Jaccard… | ||
| Words in Space: A Visual Exploration of Distance, Documents, and Distributions... | PyData DC 2018 | 2018-11-16 |
Argues that picking a distance metric matters more than the topic name and shows concrete comparisons: Euclidean, Manhattan, Chebyshev, Minkowski, Mahalanobis… | ||
| Yellowbrick: Steering Machine Learning with Visual Transformers | PyData London 2017 | 2017-05-05 |
Yellowbrick is a pure‑Python library that extends Scikit‑Learn with a core Visualizer object so Visualizers fit and transform inside pipelines and produce… | ||
| Visual diagnostics for more informed machine learning | PyData DC 2016 | 2016-10-07 |
Argument: adding visual diagnostics at each pipeline stage makes machine learning more informed, not just prettier. She demonstrates concrete tools… | ||
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