Justin Norman
where else to find them
linkedin:justindnorman
what they talk about
all 4 in the filters
where they keep going back
- TWIMLcon: AI Platforms2 editions2019–2021
on a stage with
- Divya jainonce
- Kirk Borneonce
who talks about the same things
nobody close enough yet
| title | conference | date |
|---|---|---|
| Building the Business case for ML Platforms | TWIMLcon: AI Platforms 2021 | 2021-01-19 |
Argues that an ML platform proves value by concrete outcomes not promises: reduced deployment time 6–9 months to ~3 months and fewer duplicated models across… | ||
| ML Product Experiments at Scale | TWIMLcon: AI Platforms 2021 | 2021-01-19 |
Yelp runs a homegrown experimentation platform called Bunsen that serves ML and product tests across production, running about 700 experiments concurrently and… | ||
| Why is Production ML so Hard? | TWIMLcon: AI Platforms 2019 | 2019-10-01 |
Claims that production ML fails because the model is only the small part of a larger system, and organizations trip over three concrete issues: organizational… | ||
| Federated ML for Edge Applications | Strata Data Conference 2018 | 2018-09-11 |
Fast Forward Labs at Cloudera describes federated ML that trains or partially trains models on-device and shares only parameter updates so raw text or images… | ||
| nothing matches | ||