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Deploy large language models for a healthtech use case on Amazon SageMaker

AWS Machine Learning - AI

To support overarching pharmacovigilance activities, our pharmaceutical customers want to use the power of machine learning (ML) to automate the adverse event detection from various data sources, such as social media feeds, phone calls, emails, and handwritten notes, and trigger appropriate actions.

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Generative AI in enterprises: LLM orchestration holds the key to success

CIO

Many enterprises are accelerating their artificial intelligence (AI) plans, and in particular moving quickly to stand up a full generative AI (GenAI) organization, tech stacks, projects, and governance. We think this is a mistake, as the success of GenAI projects will depend in large part on smart choices around this layer.

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AI, Cybersecurity and the Rise of Large Language Models

Palo Alto Networks

Artificial intelligence (AI) plays a crucial role in both defending against and perpetrating cyberattacks, influencing the effectiveness of security measures and the evolving nature of threats in the digital landscape. A large language model (LLM) is a state-of-the-art AI system, capable of understanding and generating human-like text.

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LLM experimentation at scale using Amazon SageMaker Pipelines and MLflow

AWS Machine Learning - AI

Large language models (LLMs) have achieved remarkable success in various natural language processing (NLP) tasks, but they may not always generalize well to specific domains or tasks. You may need to customize an LLM to adapt to your unique use case, improving its performance on your specific dataset or task.

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How to build a safe path to AI in Healthcare

CIO

This is evident in the rigorous training required for providers, the stringent safety protocols for life sciences professionals, and the stringent data and privacy requirements for healthcare analytics software. Healthcare adheres to an elevated standard. The stakes in healthcare are higher, as errors can have life-or-death consequences.

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Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering

AWS Machine Learning - AI

In this post, we share how we analyzed the feedback data and identified limitations of accuracy and hallucinations RAG provided, and used the human evaluation score to train the model through reinforcement learning. To increase training samples for better learning, we also used another LLM to generate feedback scores.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning - AI

Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. We then discuss how building on a secure foundation is essential for generative AI.