Deploy DeepSeek-R1 distilled models on Amazon SageMaker using a Large Model Inference container

Favorite DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses reinforcement learning to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key distinguishing feature is its reinforcement learning step, which was used to refine the model’s responses beyond the standard pre-training

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Shared by AWS Machine Learning March 11, 2025

Benchmarking Amazon Nova and GPT-4o models with FloTorch

Favorite Based on original post by Dr. Hemant Joshi, CTO, FloTorch.ai A recent evaluation conducted by FloTorch compared the performance of Amazon Nova models with OpenAI’s GPT-4o. Amazon Nova is a new generation of state-of-the-art foundation models (FMs) that deliver frontier intelligence and industry-leading price-performance. The Amazon Nova family of

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Shared by AWS Machine Learning March 11, 2025

Amazon Bedrock announces general availability of multi-agent collaboration

Favorite Today, we’re announcing the general availability (GA) of multi-agent collaboration on Amazon Bedrock. This capability allows developers to build, deploy, and manage networks of AI agents that work together to execute complex, multi-step workflows efficiently. Since its preview launch at re:Invent 2024, organizations across industries—including financial services, healthcare, supply

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Shared by AWS Machine Learning March 10, 2025

Optimize reasoning models like DeepSeek with prompt optimization on Amazon Bedrock

Favorite DeepSeek-R1 models, now available on Amazon Bedrock Marketplace, Amazon SageMaker JumpStart, as well as a serverless model on Amazon Bedrock, were recently popularized by their long and elaborate thinking style, which, according to DeepSeek’s published results, lead to impressive performance on highly challenging math benchmarks like AIME-2024 and MATH-500,

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Shared by AWS Machine Learning March 10, 2025

Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

Favorite Investment professionals face the mounting challenge of processing vast amounts of data to make timely, informed decisions. The traditional approach of manually sifting through countless research documents, industry reports, and financial statements is not only time-consuming but can also lead to missed opportunities and incomplete analysis. This challenge is

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Shared by AWS Machine Learning March 10, 2025

Announcing general availability of Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics

Favorite Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics. This capability enhances responses from generative AI applications by automatically creating embeddings for semantic

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Shared by AWS Machine Learning March 7, 2025

Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS

Favorite In the rapidly evolving landscape of artificial intelligence, Retrieval Augmented Generation (RAG) has emerged as a game-changer, revolutionizing how Foundation Models (FMs) interact with organization-specific data. As businesses increasingly rely on AI-powered solutions, the need for accurate, context-aware, and tailored responses has never been more critical. Enter the powerful

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Shared by AWS Machine Learning March 6, 2025

Build a Multi-Agent System with LangGraph and Mistral on AWS

Favorite Agents are revolutionizing the landscape of generative AI, serving as the bridge between large language models (LLMs) and real-world applications. These intelligent, autonomous systems are poised to become the cornerstone of AI adoption across industries, heralding a new era of human-AI collaboration and problem-solving. By using the power of

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Shared by AWS Machine Learning March 6, 2025

Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

Favorite Generative AI question-answering applications are pushing the boundaries of enterprise productivity. These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. However, building and deploying trustworthy AI assistants requires a robust ground

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Shared by AWS Machine Learning March 5, 2025