Unleash AI innovation with Amazon SageMaker HyperPod

Favorite The rise of generative AI has significantly increased the complexity of building, training, and deploying machine learning (ML) models. It now demands deep expertise, access to vast datasets, and the management of extensive compute clusters. Customers also face the challenges of writing specialized code for distributed training, continuously optimizing

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Shared by AWS Machine Learning March 18, 2025

Build your gen AI–based text-to-SQL application using RAG, powered by Amazon Bedrock (Claude 3 Sonnet and Amazon Titan for embedding)

Favorite SQL is one of the key languages widely used across businesses, and it requires an understanding of databases and table metadata. This can be overwhelming for nontechnical users who lack proficiency in SQL. Today, generative AI can help bridge this knowledge gap for nontechnical users to generate SQL queries

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Shared by AWS Machine Learning March 18, 2025

Amazon Bedrock Guardrails announces IAM Policy-based enforcement to deliver safe AI interactions

Favorite As generative AI adoption accelerates across enterprises, maintaining safe, responsible, and compliant AI interactions has never been more critical. Amazon Bedrock Guardrails provides configurable safeguards that help organizations build generative AI applications with industry-leading safety protections. With Amazon Bedrock Guardrails, you can implement safeguards in your generative AI applications

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Shared by AWS Machine Learning March 18, 2025

NeMo Retriever Llama 3.2 text embedding and reranking NVIDIA NIM microservices now available in Amazon SageMaker JumpStart

Favorite Today, we are excited to announce that the NeMo Retriever Llama3.2 Text Embedding and Reranking NVIDIA NIM microservices are available in Amazon SageMaker JumpStart. With this launch, you can now deploy NVIDIA’s optimized reranking and embedding models to build, experiment, and responsibly scale your generative AI ideas on AWS.

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Shared by AWS Machine Learning March 18, 2025

Running NVIDIA NeMo 2.0 Framework on Amazon SageMaker HyperPod

Favorite This post is cowritten with Abdullahi Olaoye, Akshit Arora and Eliuth Triana Isaza at NVIDIA. As enterprises continue to push the boundaries of generative AI, scalable and efficient model training frameworks are essential. The NVIDIA NeMo Framework provides a robust, end-to-end solution for developing, customizing, and deploying large-scale AI

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Shared by AWS Machine Learning March 18, 2025

Intelligent healthcare assistants: Empowering stakeholders with personalized support and data-driven insights

Favorite Large language models (LLMs) have revolutionized the field of natural language processing, enabling machines to understand and generate human-like text with remarkable accuracy. However, despite their impressive language capabilities, LLMs are inherently limited by the data they were trained on. Their knowledge is static and confined to the information

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Shared by AWS Machine Learning March 17, 2025

Evaluating RAG applications with Amazon Bedrock knowledge base evaluation

Favorite Organizations building and deploying AI applications, particularly those using large language models (LLMs) with Retrieval Augmented Generation (RAG) systems, face a significant challenge: how to evaluate AI outputs effectively throughout the application lifecycle. As these AI technologies become more sophisticated and widely adopted, maintaining consistent quality and performance becomes

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Shared by AWS Machine Learning March 14, 2025

Getting started with computer use in Amazon Bedrock Agents

Favorite Computer use is a breakthrough capability from Anthropic that allows foundation models (FMs) to visually perceive and interpret digital interfaces. This capability enables Anthropic’s Claude models to identify what’s on a screen, understand the context of UI elements, and recognize actions that should be performed such as clicking buttons,

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Shared by AWS Machine Learning March 14, 2025

Optimize hosting DeepSeek-R1 distilled models with Hugging Face TGI on Amazon SageMaker AI

Favorite DeepSeek-R1, developed by AI startup DeepSeek AI, is an advanced large language model (LLM) distinguished by its innovative, multi-stage training process. Instead of relying solely on traditional pre-training and fine-tuning, DeepSeek-R1 integrates reinforcement learning to achieve more refined outputs. The model employs a chain-of-thought (CoT) approach that systematically breaks

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Shared by AWS Machine Learning March 13, 2025

Revolutionizing customer service: MaestroQA’s integration with Amazon Bedrock for actionable insight

Favorite This post is cowritten with Harrison Hunter is the CTO and co-founder of MaestroQA. MaestroQA augments call center operations by empowering the quality assurance (QA) process and customer feedback analysis to increase customer satisfaction and drive operational efficiencies. They assist with operations such as QA reporting, coaching, workflow automations,

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Shared by AWS Machine Learning March 13, 2025