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10 GitHub Repositories for Machine Learning Projects

KDnuggets

Explore these top machine learning repositories to build your skills, portfolio, and creativity through hands-on projects, real-world challenges, and AI resources.

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Serve Machine Learning Models via REST APIs in Under 10 Minutes

KDnuggets

By Kanwal Mehreen , KDnuggets Technical Editor & Content Specialist on July 4, 2025 in Machine Learning Image by Author | Canva If you like building machine learning models and experimenting with new stuff, that’s really cool — but to be honest, it only becomes useful to others once you make it available to them.

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Top 5 Frameworks for Distributed Machine Learning

KDnuggets

Use these frameworks to optimize memory and compute resources, scale your machine learning workflow, speed up your processes, and reduce the overall cost.

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Accelerate Machine Learning Model Serving With FastAPI and Redis Caching

Analytics Vidhya

Machine learning models, especially the large, complex ones, can be painfully slow to serve in real time. Technically speaking, one of the biggest problems is […] The post Accelerate Machine Learning Model Serving With FastAPI and Redis Caching appeared first on Analytics Vidhya. We have all been there.

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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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7 AI Agent Frameworks for Machine Learning Workflows in 2025

Machine Learning Mastery

Machine learning practitioners spend countless hours on repetitive tasks: monitoring model performance, retraining pipelines, data quality checks, and experiment tracking.

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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.