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# aiatscale2023
Daniel Jeffries – AIIA – The Age of Industrialized AI and What It Means for the Future
Daniel Jeffries
26
Greg Throne – FeatureBase – Maintaining Up-to-the-Millisecond Freshness Across 20B Devices
Greg Throne
39
Luca Antiga – Lightning AI – Scaling Up Foundation Models with Lightning
Luca Antiga
Amber Roberts – Arize – Fairness Metrics and Bias Tracing in Production
Amber Roberts
Miriam Santos – DCAI Community – Auditing Data Quality with Pandas Profiling
Miriam Santos
Eric Landau – Encord – Active Learning & the ML Team of the Future
Eric Landau
Gilad Shaham – Iguazio – Production-First Approach: How to Scale up your ML Operations
Gilad Shaham
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AI at Scale 2023
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Data-Centric AI Summit 2022
34 Items
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Amber Roberts · Feb 23rd, 2023
Amber Roberts – Arize – Fairness Metrics and Bias Tracing in Production
# aiatscale2023
24
Miriam Santos · Feb 23rd, 2023
Miriam Santos – DCAI Community – Auditing Data Quality with Pandas Profiling
# aiatscale2023
19
Eric Landau · Feb 23rd, 2023
Eric Landau – Encord – Active Learning & the ML Team of the Future
# aiatscale2023
20
Gilad Shaham · Feb 23rd, 2023
Gilad Shaham – Iguazio – Production-First Approach: How to Scale up your ML Operations
# aiatscale2023
20
Jennifer Prendki · Feb 23rd, 2023
Jennifer Prendki – Alectio – What the *%& is DataPrepOps?
# aiatscale2023
30
Fabiana Clemente · Feb 23rd, 2023
Fabiana Clemente – YData – Scaling DataPrepOps
# aiatscale2023
20
Alejandro (Alex) Muller · Feb 23rd, 2023
Alejandro (Alex) Muller – Savvy AI – Mind the GAP
# aiatscale2023
21
Daniel Langkilde · Feb 23rd, 2023
Daniel Langkilde – Kognic – Optimal Representations for Human Feedback
# aiatscale2023
29
Niccolò Zanichelli · Feb 23rd, 2023
Niccolò Zanichelli – OpenBioML – AI at scale in computational biology
# aiatscale2023
29
Seth Clark · Feb 23rd, 2023
Seth Clark – Modzy – Deploy once, run anywhere: A new approach for scaling production ML
# aiatscale2023
26
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