Lukas Biewald
where else to find them
what they talk about
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where they keep going back
- Scale By the Bay2 editions2018–2021
all 5 in the filters
on a stage with
- Alexy Khrabrovonce
- Leila Chirayath Janahonce
- Lyle Ungaronce
all 7 in the filters
who talks about the same things
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| title | conference | date |
|---|---|---|
| Reproducible Machine Learning at Scale | Scale By the Bay 2021 | 2021-10-28 |
Shows how teams use Weights & Biases to track datasets, version training recipes, and store run metadata so experiments are auditable and shareable… | ||
| intro video | Scale By the Bay 2021 | 2021-10-28 |
An experiment-tracking platform called Weights & Biases is now used by thousands of ML teams deploying models across automotive, pharma, ag‑tech and B2B… | ||
| Hyperparameter Tuning and Visualization in Deep Learning | Ray Summit 2020 | 2020-09-30 |
Practical recipe for automated tuning: use Weights & Biases Sweeps with Ray Tune integration to run random, Bayesian, and population-based searches, add… | ||
| Interview | Scale By The Bay 2018 | 2018-11-15 |
Weights & Biases builds experiment tracking tools for machine learning teams and watches hundreds of models across industries; the concrete recommendation is… | ||
| The Real Future of AI Panel | AI By the Bay 2017 | 2017-03-06 |
This talk video is a node in devreal.ai, the developer community graph.
It connects to:
Talk: The Real Future of AI Panel —… | ||
| Interview | AI By the Bay 2017 | 2017-03-06 |
Argues that practical AI succeeds when massive labeled examples exist — cites billions of selfies powering Facebook face recognition and parallel corpora… | ||
| DBTB INT Lukas Biewald r | Data By the Bay 2016 | 2016-05-16 |
Says data volume and access have grown so many projects now start by finding previously unused public sources and building simple pipelines to test ideas… | ||
| Active Learning and Human-in-the-Loop Machine Learning | Data By the Bay 2016 | 2016-05-16 |
Argues that model gains come more from larger and cleaner labeled datasets than from tweaking algorithms, showing a toy result where error falls from ~13% at… | ||
| Lukas Biewald interviewed at Web 2.0 Expo New York | Web 2.0 Expo New York 2010 | 2010-09-27 |
Defines a practical split between crowdsourcing vs distributed work, citing LiveOps and oDesk/Elance/Guru/TopCoder as transactional distributed labor while… | ||
| The Future of Work | Web 2.0 Expo New York 2010 | 2010-09-27 |
Argues that modern AI creates jobs because supervised machine learning needs massive labeled data and humans to do tasks machines cannot, notifies a $12… | ||
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