Jennifer Prendki
where else to find them
linkedin:jennifer-prendki
what they talk about
all 5 in the filters
where they keep going back
- TWIMLcon: AI Platforms2 editions2019–2021
all 4 in the filters
on a stage with
- Eric Colsononce
- Maribel Lopezonce
- Pardis Noorzadonce
who talks about the same things
- Chris Fregly23 talks
- Murray Thom4 talks
- David Ryan5 talks
- Fanny Bouton2 talks
- Natalie Fisher3 talks
- Alireza Rahmani3 talks
| title | conference | date |
|---|---|---|
| The Next Compute Revolution: Are We Ready for Quantum? | AI_dev Europe 2025 | 2025-08-28 |
Claim: useful quantum machines are nearer than the “30 years away” refrain and already power niche wins in search and optimization, quantum chemistry and… | ||
| Data Prep Ops: The Missing Piece of the MLOps Puzzle | TWIMLcon: AI Platforms 2021 | 2021-01-19 |
Claim: poor AI ROI comes from failing to treat dataset work as engineering. Company Alectio builds a pipeline that prioritizes data selection (useful vs… | ||
| When the Only Way to Scale up, is to Scale Down | Scale By the Bay 2020 | 2020-11-12 |
Claims that less data often beats more data and shows tooling to prove it: Alectio uses active learning and an auto active learning optimizer to pick training… | ||
| Culture & Organization for Effective ML at Scale | TWIMLcon: AI Platforms 2019 | 2019-10-01 |
Companies fail when ML is treated as a one-off instead of a running product: models need lifecycle management, monitoring and regular retraining rather than a… | ||
| Review Analysis: an Approach to Leveraging User-Generated... | MLconf SF 2016 | 2016-11-11 |
Pipeline extracts product-specific decision features via tf-idf + word2vec and an entity graph, runs spellchecking and POS filters, then scores per-feature… | ||
| nothing matches | ||