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Hi everyone, Great news! We've recently extended the deadline on our 50% voucher code for ANY certification in...
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Hi everyone, Great news! We've recently extended the deadline on our 50% voucher code for ANY certification in...
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In many real-world applications, we encounter tasks that sit at the intersection of semantic understanding and contextual judgment. One such common scenario is determining if a set of keywords truly aligns with a brand, its products, and its messaging (like a tagline). This isn't just about whether the words are synonyms; it's about relevance in a specific context. Does "eco-friendly" align well with a fast-fashion brand's new line, even if the tagline mentions "sustainable materials"? Does "high-performance" fit a budget-friendly product?
Metadata is essential for various data initiatives, from deploying NLP solutions and chat with your data applications to enabling semantic search and establishing a business glossary. However, the manual effort of documenting every data attribute within a platform can be time-consuming and error-prone. At Google Cloud Next, we announced BigQuery Data Insights and Automated Metadata Generation, two powerful capabilities that simplify this process using Gen AI.
Ready to unlock the secrets hidden in your product data? This tutorial demonstrates how BigQuery Data Preparation can clean and transform your raw data into actionable business intelligence, using a realistic example from the Fashion and Beauty industry.