Flipboard

DECEMBER 12, 2024

AI, machine learning (ML), and data science infuse our daily lives, from the recommendation functionality on music apps to technologies that

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NYU Center for Data Science

SEPTEMBER 28, 2023

Building on this momentum is a dynamic research group at the heart of CDS called the Machine Learning and Language (ML²) group. By 2020, ML² was a thriving community, primarily known for its recurring speaker series where researchers presented their work to peers. What does it mean to work in NLP in the age of LLMs?

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Towards AI

APRIL 25, 2024

Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! Louis-François Bouchard, Towards AI Co-founder & Head of Community Learn AI Together Community section! Join us next Saturday, April 27, for the RAG paper walkthrough on the Learn AI Together Discord server!

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Towards AI

NOVEMBER 16, 2023

Last Updated on November 17, 2023 by Editorial Team Author(s): Towards AI Editorial Team Originally published on Towards AI. Learn to Build — Towards AI Community Newsletter Welcome again to this first iteration of our community newsletter — a now separated entity from our usual weekly news/paper-oriented newsletter.

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Towards AI

APRIL 7, 2024

Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. Additionally, the elimination of human loop processes has made it possible for AI/ML to construct training data for data annotation and labeling, which has a major influence on geospatial data. This will be a good way to get familiar with ML.

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AWS Machine Learning Blog

APRIL 22, 2025

Infosys , a leading global IT services and consulting organization, used its digital expertise to tackle this challenge by pioneering, Infosys Event AI, an innovative AI-based event assistant. The following screenshot shows a brief demo based on a fictitious scenario to illustrate Event AIs real-time streaming capability.

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. Supporting the operations of data scientists and ML engineers requires you to reduce—or eliminate—the engineering overhead of building, deploying, and maintaining high-performance models.

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