Flavia Saldanha, a consulting knowledge engineer, joins host Kanchan Shringi to debate the evolution of information engineering from ETL (extract, remodel, load) and knowledge lakes to trendy lakehouse architectures enriched with vector databases and embeddings. Flavia explains the business’s shift from treating knowledge as a service to treating it as a product, emphasizing possession, belief, and enterprise context as crucial for AI-readiness. She describes how unified pipelines now serve each enterprise intelligence and AI use instances, combining structured and unstructured knowledge whereas making certain semantic enrichment and a single supply of fact. She outlines key parts of a contemporary knowledge stack, together with knowledge marketplaces, observability instruments, knowledge high quality checks, orchestration, and embedded governance with lineage monitoring. The episode highlights methods for abstracting tooling, future-proofing architectures, imposing knowledge privateness, and controlling AI-serving layers to stop hallucinations. Saldanha concludes that knowledge engineers should transfer past pure ETL considering, embrace product and NLP expertise, and work carefully with MLOps, utilizing AI as a co-pilot relatively than a alternative.
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