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Configuring Amazon Bedrock AgentCore Gateway for secure access to private resources

Favorite AI agents in production environments often need to reach internal APIs, databases, and private resources that sit behind Amazon Virtual Private Cloud (Amazon VPC) boundaries. Managing private connectivity for each agent-to-tool path adds operational overhead and slows deployment. Amazon Bedrock AgentCore VPC connectivity is designed to deploy AI agents

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Shared by AWS Machine Learning April 30, 2026

Unleashing Agentic AI Analytics on Amazon SageMaker with Amazon Athena and Amazon Quick

Favorite Modern enterprises face mounting challenges in extracting actionable insights from vast data lakes and lakehouses spanning petabytes of structured and unstructured data. Traditional analytics require specialized technical expertise in SQL, data modeling, and business intelligence tools, creating bottlenecks that slow decision-making across retail, financial services, healthcare, Travel & Hospitality,

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Shared by AWS Machine Learning April 30, 2026

Sun Finance automates ID extraction and fraud detection with generative AI on AWS

Favorite This post was co-authored with Krišjānis Kočāns, Kaspars Magaznieks, Sergei Kiriasov from Sun Finance Group If you process identity documents at scale—loan applications, account openings, compliance checks—you’ve likely hit the same wall: traditional optical character recognition (OCR) gets you partway there, but extraction errors still push a large share

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Shared by AWS Machine Learning April 30, 2026

AWS Generative AI Model Agility Solution: A comprehensive guide to migrating LLMs for generative AI production

Favorite Maintaining model agility is crucial for organizations to adapt to technological advancements and optimize their artificial intelligence (AI) solutions. Whether transitioning between different large language model (LLM) families or upgrading to newer versions within the same family, a structured migration approach and a standardized process are essential for facilitating

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Shared by AWS Machine Learning April 30, 2026

Reinforcement fine-tuning with LLM-as-a-judge

Favorite Large language models (LLMs) now drive the most advanced conversational agents, creative tools, and decision-support systems. However, their raw output often contains inaccuracies, policy misalignments, or unhelpful phrasing—issues that undermine trust and limit real-world utility. Reinforcement Fine‑Tuning (RFT) has emerged as the preferred method to align these models efficiently,

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Shared by AWS Machine Learning April 30, 2026

Building AI-ready data: Vanguard’s Virtual Analyst journey

Favorite Vanguard is a global investment management firm, offering a broad selection of investments, advice, retirement services, and insights to individual investors, institutions, and financial professionals. We operate under a unique, investor-owned structure and adhere to a straightforward purpose: To take a stand for all investors, to treat them fairly,

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Shared by AWS Machine Learning April 29, 2026

Organizing Agents’ memory at scale: Namespace design patterns in AgentCore Memory

Favorite When building AI agents, developers struggle with organizing memory across sessions, which leads to irrelevant context retrieval and security vulnerabilities. AI agents that remember context across sessions need more than only storage. They need organized, retrievable, and secure memory. In Amazon Bedrock AgentCore Memory, namespaces determine how long-term memory

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Shared by AWS Machine Learning April 29, 2026

Extracting contract insights with PwC’s AI-driven annotation on AWS

Favorite This post was co-written with Yash Munsadwala, Adam Hood, Justin Guse, and Hector Hernandez from PwC. Contract analysis often consumes significant time for legal, compliance, and procurement teams, especially when important insights are buried in lengthy, unstructured agreements. As contract volumes grow, finding specific clauses and assessing extracted terms

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Shared by AWS Machine Learning April 29, 2026