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ML and AI Model Explainability and Interpretability

Analytics Vidhya

In this article, we dive into the concepts of machine learning and artificial intelligence model explainability and interpretability. We explore why understanding how models make predictions is crucial, especially as these technologies are used in critical fields like healthcare, finance, and legal systems.

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Machine unlearning: Researchers make AI models ‘forget’ data

AI News

Researchers from the Tokyo University of Science (TUS) have developed a method to enable large-scale AI models to selectively “forget” specific classes of data. Progress in AI has provided tools capable of revolutionising various domains, from healthcare to autonomous driving.

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LitServe: The Future of Scalable AI Model Serving

Analytics Vidhya

Introduction While FastAPI is good for implementing RESTful APIs, it wasn’t specifically designed to handle the complex requirements of serving machine learning models. FastAPI’s support for asynchronous calls is primarily at the web level and doesn’t extend deeply into the model prediction layer.

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Top AI Models are Getting Lost in Long Documents

Unite.AI

A new study from researchers at LMU Munich, the Munich Center for Machine Learning, and Adobe Research has exposed a weakness in AI language models : they struggle to understand long documents in ways that might surprise you. Many AI models, it turns out, do not work this way at all.

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Implementing Advanced Analytics in Real Estate: Using Machine Learning to Predict Market Shifts

Unite.AI

Machine learning has disrupted many industries over the past few years, but the effects it has had in the real estate market fluctuation forecasting area have been nothing short of transformative. From 2025 onwards, machine learning will no longer be a utility but a strategic advantage in how real estate is approached.

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Nordic Startup IntuiCell Unveils World’s First Digital Nervous System for AI

Unite.AI

IntuiCell , a spin-out from Lund University, revealed on March 19, 2025, that they have successfully engineered AI that learns and adapts like biological organisms, potentially rendering current AI paradigms obsolete in many applications. The practical application of this technology reflects its biological inspiration.

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How iFood built a platform to run hundreds of machine learning models with Amazon SageMaker Inference

AWS Machine Learning Blog

With the support of AWS, iFood has developed a robust machine learning (ML) inference infrastructure, using services such as Amazon SageMaker to efficiently create and deploy ML models. Data scientists would build models using notebooks, adjust weights, and publish them onto services.