Data engineers are becoming frustrated with the number of disjointed tools and solutions required to build production-ready pipelines. Without a centralized data intelligence platform or unified governance, teams deal with …
Tag:
Engineering
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Image by author # Introduction As a machine learning practitioner, you know that feature engineering is laborious, manual work. You need to create interaction terms between features, properly encode categorical …
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Generative AI
Meta and Harvard researchers introduce the Confucian Code Agent (CCA): a software engineering agent that can operate on massive codebases
How far can a medium-sized language model go if the real innovation moves from the backbone to the agent scaffolding and tool stack? Meta and Harvard researchers have released Confucius …
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Generative AI
How to build a portable, in-database feature engineering pipeline with Ibis using lazy Python API and DuckDB execution
In this tutorial, we demonstrate how we use A type of bird To create a portable, in-database feature engineering pipeline that looks and feels like Pandas but executes entirely inside …
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Jaden Chizuruoke May ’29 worked with teammates Rihanna Arouna ’29 and Marion Akinsoji ’29 to design a chemically powered model car, the framework of which he is building in this …
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Image by author , Introduction Building Extract, Transform, Load (ETL) pipelines is one of the many responsibilities data engineerWhile you can create ETL pipelines using Pure Python And PandaSpecialized tools …
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Reference engineering explained in 3 levels of difficulty. Image by author , Introduction Large language model (LLM) applications constantly affect context window boundaries. The model forgets earlier instructions, loses track …
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Illustration by Tag Hartman-Simkins/Futurism. Source: Getty Images It is almost impossible for graduates of some top universities to find new jobs in software engineering. New reporting by Los Angeles Times …
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Image by editor , Introduction Data engineering is quietly undergoing one of its most consequential changes in a decade. The familiar problems of scale, reliability, and cost haven’t gone away, …
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Image by editor , Introduction Instead of relying solely on static rules or regex patterns, data teams are now exploring Well-crafted prompts can help identify anomalies, inconsistencies, and outright errors …