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Fixing Your ML Data Blind Spots

Posted Jun 09
# Unstructure Data
# ML Workflow
# Galileo
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SPEAKER
Yash Sheth
Yash Sheth
Yash Sheth
Co-founder and VP of Engineering @ Galileo

Co-founder and VP of Engineering. Prior to starting Galileo, Yash spent the last decade working on Automatic Speech Recognition (ASR) at Google, leading their core speech recognition platform team, that powers speech-to-text across 20+ products at Google in over 80 languages along with thousands of businesses through their Cloud Speech API.

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Co-founder and VP of Engineering. Prior to starting Galileo, Yash spent the last decade working on Automatic Speech Recognition (ASR) at Google, leading their core speech recognition platform team, that powers speech-to-text across 20+ products at Google in over 80 languages along with thousands of businesses through their Cloud Speech API.

+ Read More
SUMMARY

Improving your dataset quality is absolutely critical for effective ML. Finding errors in your datasets is generally a slow, iterative, and painstaking process.

Data scientists should be proactively fixing their model’s blindspots by improving their training data. In this talk, Yash discusses how Galileo helps data scientists identify, fix, and track data across the entire ML workflow.

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# Data Modeling
# Data Warehouses
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