Remove Data Discovery Remove Data Quality Remove Large Language Models
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Why data governance is essential for enterprise AI

IBM Journey to AI blog

The recent success of artificial intelligence based large language models has pushed the market to think more ambitiously about how AI could transform many enterprise processes. However, consumers and regulators have also become increasingly concerned with the safety of both their data and the AI models themselves.

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12 AI Insight Talks to Help Improve Your Company’s AI Game at ODSC West

ODSC - Open Data Science

This AI Insight talk will showcase how VESSL AI enables enterprises to scale the deployment of over 100+ Large Language Models (LLMs) starting at just $10, helping businesses save substantial cloud costs — up to $100K annually. Delphina Demo: AI-powered Data Scientist Jeremy Hermann | Co-founder at Delphina | Delphina.Ai

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Google experts on practical paths to data-centricity in applied AI

Snorkel AI

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

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Google experts on practical paths to data-centricity in applied AI

Snorkel AI

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

article thumbnail

Google experts on practical paths to data-centricity in applied AI

Snorkel AI

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.