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AI at Scale 2023
# aiatscale2023
Fabiana Clemente – YData – Scaling DataPrepOps
Fabiana Clemente · Feb 23rd, 2023
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Amber Roberts · Feb 23rd, 2023
Amber Roberts – Arize – Fairness Metrics and Bias Tracing in Production
# aiatscale2023
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Alejandro (Alex) Muller · Feb 23rd, 2023
Alejandro (Alex) Muller – Savvy AI – Mind the GAP
# aiatscale2023
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Eric Landau · Feb 23rd, 2023
Eric Landau – Encord – Active Learning & the ML Team of the Future
# aiatscale2023
20
Jennifer Prendki · Feb 23rd, 2023
Jennifer Prendki – Alectio – What the *%& is DataPrepOps?
# aiatscale2023
30
Miriam Santos · Feb 23rd, 2023
Miriam Santos – DCAI Community – Auditing Data Quality with Pandas Profiling
# aiatscale2023
19
Gilad Shaham · Feb 23rd, 2023
Gilad Shaham – Iguazio – Production-First Approach: How to Scale up your ML Operations
# aiatscale2023
20
Bernease Herman · Feb 23rd, 2023
Bernease Herman –WhyLabs–Truly scalable monitoring and observability for massive NLP and CV datasets
# aiatscale2023
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Amit Phadke · Feb 23rd, 2023
Amit Phadke – Bosch AIShield – Securing AI systems at Scale
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Daniel Langkilde · Feb 23rd, 2023
Daniel Langkilde – Kognic – Optimal Representations for Human Feedback
# aiatscale2023
29
Herbie Bradley · Feb 23rd, 2023
Herbie Bradley – EleutherAI – Speeding up inference of LLMs with Triton and FasterTransformer
# aiatscale2023
10
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