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Unlearning Copyrighted Data From a Trained LLM – Is It Possible?

Unite.AI

In the domains of artificial intelligence (AI) and machine learning (ML), large language models (LLMs) showcase both achievements and challenges. Trained on vast textual datasets, LLM models encapsulate human language and knowledge. Why is LLM Unlearning Needed?

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Establishing an AI/ML center of excellence

AWS Machine Learning Blog

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. According to a McKinsey study , across the financial services industry (FSI), generative AI is projected to deliver over $400 billion (5%) of industry revenue in productivity benefits.

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LLMOps: The Next Frontier for Machine Learning Operations

Unite.AI

Machine learning (ML) is a powerful technology that can solve complex problems and deliver customer value. However, ML models are challenging to develop and deploy. This is why Machine Learning Operations (MLOps) has emerged as a paradigm to offer scalable and measurable values to Artificial Intelligence (AI) driven businesses.

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Foundational data protection for enterprise LLM acceleration with Protopia AI

AWS Machine Learning Blog

New and powerful large language models (LLMs) are changing businesses rapidly, improving efficiency and effectiveness for a variety of enterprise use cases. Speed is of the essence, and adoption of LLM technologies can make or break a business’s competitive advantage. This optimization pass is delivered through an extension to PyTorch.

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Large Language Model Ops (LLM Ops)

Mlearning.ai

Introduction Create ML Ops for LLM’s Build end to end development and deployment cycle. Add Responsible AI to LLM’s Add Abuse detection to LLM’s. High level process and flow LLM Ops is people, process and technology. LLM Ops flow — Architecture Architecture explained.

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Navigating the Complexity of Trustworthiness in LLMs: A Deep Dive into the TRUST LLM Framework

Marktechpost

A large team of Researchers from world-class universities, institutions, and labs have introduced a comprehensive framework, TRUST LLM. The TRUST LLM framework aims to establish a benchmark for evaluating these aspects in mainstream LLMs. The TRUST LLM framework offers a nuanced approach to evaluating large language models.

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The Black Box Problem in LLMs: Challenges and Emerging Solutions

Unite.AI

Exploring the Techniques of LIME and SHAP Interpretability in machine learning (ML) and deep learning (DL) models helps us see into opaque inner workings of these advanced models. SHAP ( Source ) Both LIME and SHAP have emerged as essential tools in the realm of AI and ML, addressing the critical need for transparency and trustworthiness.

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