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Build an intelligent eDiscovery solution using Amazon Bedrock Agents

Legal teams spend bulk of their time manually reviewing documents during eDiscovery. This process involves analyzing electronically stored information across emails, contracts, financial records, and collaboration systems for legal proceedings. This manual approach creates significant bottlenecks: attorneys must identify privileged communications, assess legal risks, extract contractual obligations, and maintain regulatory compliance across thousands of documents …

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Your guide to taking an open model from discovery to a production-ready endpoint on Vertex AI

Developers building with gen AI are increasingly drawn to open models for their power and flexibility. But customizing and deploying them can be a huge challenge. You’re often left wrestling with complex dependencies, managing infrastructure, and fighting for expensive GPU access. Don’t let that complexity slow you down. In this guide, we’ll walk you through …

MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains

Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, while useful, often focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes. This lack of granularity makes it difficult to deeply discern …

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Boost cold-start recommendations with vLLM on AWS Trainium

Cold start in recommendation systems goes beyond just new user or new item problems—it’s the complete absence of personalized signals at launch. When someone first arrives, or when fresh content appears, there’s no behavioral history to tell the engine what they care about, so everyone ends up in broad generic segments. That not only dampens …

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New Cluster Director features: Simplified GUI, managed Slurm, advanced observability

In April, we released Cluster Director, a unified management plane that makes deploying and managing large-scale AI infrastructure simpler and more intuitive than ever before, putting the power of an AI supercomputer at your fingertips. Today, we’re excited to release new features in preview including an intuitive interface, managed Slurm experience, and observability dashboard that …

mRAKL: Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages

Knowledge Graphs represent real-world entities and the relationships between them. Multilingual Knowledge Graph Construction (mKGC) refers to the task of automatically constructing or predicting missing entities and links for knowledge graphs in a multilingual setting. In this work, we reformulate the mKGC task as a Question Answering (QA) task and introduce mRAKL: a Retrieval-Augmented Generation …

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Customize Amazon Nova in Amazon SageMaker AI using Direct Preference Optimization

At the AWS Summit in New York City, we introduced a comprehensive suite of model customization capabilities for Amazon Nova foundation models. Available as ready-to-use recipes on Amazon SageMaker AI, you can use them to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. In this …

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Beyond accelerators: Lessons from building foundation models on AWS with Japan’s GENIAC program

In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second cycle (cycle 2). It provided infrastructure …