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For organizations introducing AI agents into employee workflows and customer experiences, data risk cannot be an afterthought. Here are seven practices to ensure your enterprise data is ready for AI.
Four big lessons, seven practical tips, three useful patterns, and five common antipatterns we learned from building an AI CRM.
For enterprises, proprietary data is a source of competitive advantage. Take these four steps to ready it for AI-powered applications and agents.
The enhancements in Horizon Catalog would allow enterprises to build context for AI-driven applications and autonomous agents from data stored outside Snowflake.
Treating annotation as a data understanding problem, rather than a labeling workflow challenge, can systematically drive down error rates and reduce the time and cost of producing high-quality data sets.
The new managed functions will let enterprises apply LLM reasoning to structured and unstructured data directly in SQL, eliminating prompt tuning and external tools.
Company plans to integrate Datometry’s technology into SnowConvert AI.
Generative AI models can carry on conversations, answer questions, write stories, produce source code, and create images and videos of almost any description. Here's how generative AI works, how it's being used, and why it’s more limi
The data mesh architecture promised to eliminate the inefficiencies and errors of the data lake. What went wrong?
AIOps isn’t just a buzzword — it helps teams predict issues before they happen and fix them automatically with smart, connected monitoring.