Governing the ML lifecycle at scale, Part 4: Scaling MLOps with security and governance controls

Favorite Data science teams often face challenges when transitioning models from the development environment to production. These include difficulties integrating data science team’s models into the IT team’s production environment, the need to retrofit data science code to meet enterprise security and governance standards, gaining access to production grade data,

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Shared by AWS Machine Learning February 8, 2025

How Aetion is using generative AI and Amazon Bedrock to translate scientific intent to results

Favorite This post is co-written with Javier Beltrán, Ornela Xhelili, and Prasidh Chhabri from Aetion.  For decision-makers in healthcare, it is critical to gain a comprehensive understanding of patient journeys and health outcomes over time. Scientists, epidemiologists, and biostatisticians implement a vast range of queries to capture complex, clinically relevant

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Shared by AWS Machine Learning February 7, 2025

Protect your DeepSeek model deployments with Amazon Bedrock Guardrails

Favorite The rapid advancement of generative AI has brought powerful publicly available large language models (LLMs), such as DeepSeek-R1, to the forefront of innovation. The DeepSeek-R1 models are now accessible through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart, and distilled variants are available through Amazon Bedrock Custom Model Import. According

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Shared by AWS Machine Learning February 7, 2025

Enhancing LLM Capabilities with NeMo Guardrails on Amazon SageMaker JumpStart

Favorite As large language models (LLMs) become increasingly integrated into customer-facing applications, organizations are exploring ways to leverage their natural language processing capabilities. Many businesses are investigating how AI can enhance customer engagement and service delivery, and facing challenges in making sure LLMs driven engagements are on topic and follow

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Shared by AWS Machine Learning February 6, 2025