How developers can simplify feature engineering

Estimated read time 6 min read



Building real-world AI tools requires getting your hands dirty with data. The challenge? Traditional data architectures often act like stubborn filing cabinets, they just don’t accommodate the volume of unstructured data we are generating.

From generative AI-powered customer service and recommendation engines to AI-powered drone deliveries and supply chain optimization, Fortune 500 retailers like Walmart deploy dozens of AI and machine learning (ML) models, each reading and producing unique combinations of datasets. This variability demands tailored data ingestion, storage, processing, and transformation components.



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