Erin Mikail Staples
Employers on the program
- 2026TigerData1 talk
- 2023LaunchDarkly3 talks
- 2023Heartex1 talk
The year around now
Talks in the past 12 months and announced talks ahead, one row per conference, January to December. Hover a mark for the talk.
Appearances by year
- PyData
- PGConf NYC (Postgres Summit US from 2026)
Talks
| Date | Event | Conference | Role | Title | Links |
|---|---|---|---|---|---|
| Sep 30, 2026 | Postgres Summit US 2026 | PGConf NYC (Postgres Summit US from 2026) | sponsored talk | Keyword, Semantic, or Both? A Field Guide to Search in Postgres as Tiger Data "Should we add Elasticsearch? Do we need a vector database?" Before you add a second system, it's worth knowing what Postgres can do today, because the answer…Show less"Should we add Elasticsearch? Do we need a vector database?" Before you add a second system, it's worth knowing what Postgres can do today, because the answer changed a lot in the last year. | |
| Nov 1, 2023 | PyData NYC 2023 | PyData | fireside chat | Fireside Chat with Michelle Gill as LaunchDarkly Have questions after a keynote? Excited to learn more from one of our keynote speakers? | |
| Nov 1, 2023 | PyData NYC 2023 | PyData | fireside chat | Fireside Chat with Soumith Chintala as LaunchDarkly Have questions after a keynote? Excited to learn more from one of our keynote speakers? | |
| Nov 1, 2023 | PyData NYC 2023 | PyData | tutorial | Shipping like a Spy; Enabling continuous deployment with stealth, security, and smooth moves in mind. as LaunchDarkly We’re moving at a speed faster than ever before, with approximately 328.77 million terabytes of data created each day. For those managing these databases,…Show lessWe’re moving at a speed faster than ever before, with approximately 328.77 million terabytes of data created each day. For those managing these databases, building a secure and sustainable process for building and releasing elements around the core database is more important than ever. | Video |
| Apr 17, 2023 | PyCon DE & PyData Berlin 2023 | PyData | talk | Improving Machine Learning from Human Feedback as Heartex Large generative models rely upon massive data sets that are collected automatically. For example, GPT-3 was trained with data from “Common Crawl” and “Web…Show lessLarge generative models rely upon massive data sets that are collected automatically. For example, GPT-3 was trained with data from “Common Crawl” and “Web Text”, among other sources. |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Aleksander Molak | 2 | — | 2023 |
| Alexander CS Hendorf | 2 | — | 2023 |
| Ines Montani | 2 | — | 2023 |
| Patrick Hoefler | 2 | — | 2023 |
| Michelle Gill | 1 | 1 | 2023 |
| Nikolai | 1 | 1 | 2023 |
| Soumith Chintala | 1 | 1 | 2023 |
Speakers with the same name are merged into one entry, so this page may combine more than one person. Affiliations are shown as each conference listed them.