Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

Favorite Evaluating large language models (LLMs) is crucial as LLM-based systems become increasingly powerful and relevant in our society. Rigorous testing allows us to understand an LLM’s capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk. Furthermore, evaluation processes are important not only for LLMs,

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Shared by AWS Machine Learning January 29, 2025

Optimizing AI responsiveness: A practical guide to Amazon Bedrock latency-optimized inference

Favorite In production generative AI applications, responsiveness is just as important as the intelligence behind the model. Whether it’s customer service teams handling time-sensitive inquiries or developers needing instant code suggestions, every second of delay, known as latency, can have a significant impact. As businesses increasingly use large language models

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Shared by AWS Machine Learning January 29, 2025

Secure a generative AI assistant with OWASP Top 10 mitigation

Favorite A common use case with generative AI that we usually see customers evaluate for a production use case is a generative AI-powered assistant. However, before it can be deployed, there is the typical production readiness assessment that includes concerns such as understanding the security posture, monitoring and logging, cost

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Shared by AWS Machine Learning January 25, 2025

Enhance your customer’s omnichannel experience with Amazon Bedrock and Amazon Lex

Favorite The rise of AI has opened new avenues for enhancing customer experiences across multiple channels. Technologies like natural language understanding (NLU) are employed to discern customer intents, facilitating efficient self-service actions. Automatic speech recognition (ASR) translates spoken words into text, enabling seamless voice interactions. With Amazon Lex bots, businesses

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Shared by AWS Machine Learning January 24, 2025

How Cato Networks uses Amazon Bedrock to transform free text search into structured GraphQL queries

Favorite This is a guest post authored by Asaf Fried, Daniel Pienica, Sergey Volkovich from Cato Networks. Cato Networks is a leading provider of secure access service edge (SASE), an enterprise networking and security unified cloud-centered service that converges SD-WAN, a cloud network, and security service edge (SSE) functions, including

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Shared by AWS Machine Learning January 23, 2025