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Why Mathematics is Essential for Data Science and Machine Learning

insideBIGDATA

Gutierrez, insideAInews Editor-in-Chief & Resident Data Scientist, explores why mathematics is so integral to data science and machine learning, with a special focus on the areas most crucial for these disciplines, including the foundation needed to understand generative AI.

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Research: A periodic table for machine learning

Dataconomy

In machine learning, few ideas have managed to unify complexity the way the periodic table once did for chemistry. Now, researchers from MIT, Microsoft, and Google are attempting to do just that with I-Con, or Information Contrastive Learning. This ballroom analogy extends to all of machine learning.

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Apple Machine Learning Research at ICLR 2025

Machine Learning Research at Apple

Apple researchers are advancing machine learning (ML) and AI through fundamental research that improves the worlds understanding of this technology and helps to redefine what is possible with it.

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Top 7 Data Science, Large Language Model, and AI Blogs of 2024

Data Science Dojo

The fields of Data Science, Artificial Intelligence (AI), and Large Language Models (LLMs) continue to evolve at an unprecedented pace. In this blog, we will explore the top 7 LLM, data science, and AI blogs of 2024 that have been instrumental in disseminating detailed and updated information in these dynamic fields.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations.

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The Data Disconnect: A Key Challenge for Machine Learning Deployment

insideBIGDATA

This article is excerpted from the book, "The AI Playbook: Mastering the Rare Art of Machine Learning Deployment," by Eric Siegel, Ph.D., with permission from the publisher, MIT Press.

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Deploying Machine Learning Models at Scale: Strategies for Efficient Production

insideBIGDATA

In this contributed article, freelance writer Ainsley Lawrence briefly explores deploying machine learning models, showing you how to manage multiple models, establish robust monitoring protocols, and efficiently prepare to scale.