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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Why Software Engineers Should Be Embracing AI: A Guide to Staying Ahead

ODSC - Open Data Science

The rapid evolution of AI is transforming nearly every industry/domain, and software engineering is no exception. But how so with software engineering you may ask? These technologies are helping engineers accelerate development, improve software quality, and streamline processes, just to name a few.

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Julian LaNeve, CTO at Astronomer – Interview Series

Unite.AI

He’s passionate about all things data and open source as he spends his spare time doing hackathons, prototyping new projects, and exploring the latest in data. Could you share your personal story of how you became involved with software engineering, and worked your way up to being CTO of Astronomer?

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning Blog

He has touched on most aspects of these projects, from infrastructure and DevOps to software development and AI/ML. After earning his bachelors degree in software engineering and a masters in computer vision and machine learning from Polytechnique Montreal, Philippe joined AWS to put his expertise to work for customers.

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Find Your AI Solutions at the ODSC West AI Expo

ODSC - Open Data Science

HPCC Systems — The Kit and Kaboodle for Big Data and Data Science Bob Foreman | Software Engineering Lead | LexisNexis/HPCC Join this session to learn how ECL can help you create powerful data queries through a comprehensive and dedicated data lake platform.

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Machine Learning Operations (MLOPs) with Azure Machine Learning

ODSC - Open Data Science

Data Estate: This element represents the organizational data estate, potential data sources, and targets for a data science project. Data Engineers would be the primary owners of this element of the MLOps v2 lifecycle. The Azure data platforms in this diagram are neither exhaustive nor prescriptive.