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The latest from Team Snorkel
Blog
How to build reusable data cleaning pipelines with scikit-learn
State Farm senior data scientist Jason Goldfarb presented “Reusable Data Cleaning Pipelines in Python” at the Future of Data-Centric AI 2022.
Blog
Arize AI on How to apply and use machine learning observability
Jack Zhou, product manager at Arize, on “How to Apply Machine Learning Observability to Your ML System” from The Future of Data-Centric AI
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Claypot AI CEO on why you should deploy models the hard way
Claypot AI CEO Chip Huyen presented “Platform for Real-Time Machine Learning” at Snorkel AI’s Future of Data-Centric AI 2022.
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McKinsey QuantumBlack on automating data quality remediation with AI
Jacomo Corbo and Bryan Richardson with QuantumBlack present “Automating Data Quality Remediation With AI” at The Future of Data-Centric AI.
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Sambanova on using LLMs to squeeze value from business data
Stefano Lindt presents “Leveraging NLP to Extract Value From Business Data” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.
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Black Swan Data CTO on how to tackle petabyte-level learning
Peter Davio, CTO at Black Swan Data, presented “Petabyte-Level Learning” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.
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How Grammarly strives for superhuman communication assistance
Grammarly’s Timo Mertens presents “Toward Superhuman Communication Assistance” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.
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How MLCommons is democratizing data with public datasets
Peter Mattson, Google senior staff engineer and president of MLCommons.org, explained MLCommons at The Future of Data-Centric AI in 2022.
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Snowflake Snowpark: cloud SQL and Python ML pipelines
Ahmad Khan, Snowflake’s head of AI /ML strategy, presented “Scalable SQL + Python ML Pipelines in the Cloud” at |The Future of Data-Centric AI in 2022.
