Amazon Machine Learning

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Pioneering AI workflows at scale: A deep dive into Asana AI Studio and Amazon Q index collaboration

Amazon Machine Learning, Amazon Q, Amazon Q Business, Customer Solutions, Generative AI, Technical How-to /

Organizations today face a critical challenge: managing an ever-increasing volume of tasks and information across multiple systems. Although traditional task management tools help organize work, they often fall short in delivering the intelligence needed for truly efficient operations. Today, we’re excited to announce the integration of Asana AI Studio with Amazon Q index, bringing generative […]

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Building an AI-driven course content generation system using Amazon Bedrock

Advanced (300), Amazon Bedrock, Amazon Machine Learning, Education, Generative AI, Higher education, Public Sector, Technical How-to /

The education sector needs efficient, high-quality course material development that can keep pace with rapidly evolving knowledge domains. Faculty invest days to create content and quizzes for topics to be taught in weeks. Increased faculty engagement in manual content creation creates a time deficit for innovation in teaching, inconsistent course material, and a poor experience

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Introducing Amazon Bedrock AgentCore Browser Tool

Amazon Bedrock, Amazon Machine Learning, Announcements, Artificial Intelligence /

At AWS Summit New York City 2025, Amazon Web Services (AWS) announced the preview of Amazon Bedrock AgentCore browser tool, a fully managed, pre-built cloud-based browser. This tool enables generative AI agents to interact seamlessly with websites. It addresses two fundamental limitations: first, foundation models (FMs) are trained on large but static datasets and need

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Introducing the Amazon Bedrock AgentCore Code Interpreter

Amazon Bedrock, Amazon Machine Learning, Announcements, Artificial Intelligence /

AI agents have reached a critical inflection point where their ability to generate sophisticated code exceeds the capacity to execute it safely in production environments. Organizations deploying agentic AI face a fundamental dilemma: although large language models (LLMs) can produce complex code scripts, mathematical analyses, and data visualizations, executing this AI-generated code introduces significant security

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Amazon Strands Agents SDK: A technical deep dive into agent architectures and observability

Amazon Bedrock, Amazon Bedrock Agents, Amazon Machine Learning, Artificial Intelligence, Generative AI, open source /

The Amazon Strands Agents SDK is an open source framework for building AI agents that emphasizes a model-driven approach. Instead of hardcoding complex task flows, Strands uses the reasoning abilities of modern large language models (LLMs) to handle planning and tool usage autonomously. Developers can create an agent with a prompt (defining the agent’s role

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How Nippon India Mutual Fund improved the accuracy of AI assistant responses using advanced RAG methods on Amazon Bedrock

Amazon Bedrock, Amazon Bedrock Guardrails, Amazon Bedrock Knowledge Bases, Amazon Machine Learning, Artificial Intelligence, Customer Solutions /

This post is co-written with Abhinav Pandey from Nippon Life India Asset Management Ltd. Accurate information retrieval through generative AI-powered assistants is a popular use case for enterprises. To reduce hallucination and improve overall accuracy, Retrieval Augmented Generation (RAG) remains the most commonly used method to retrieve reliable and accurate responses that use enterprise data

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Optimizing enterprise AI assistants: How Crypto.com uses LLM reasoning and feedback for enhanced efficiency

Amazon Bedrock, Amazon Machine Learning, Artificial Intelligence, Customer Solutions /

This post is co-written with Jessie Jiao from Crypto.com. Crypto.com is a crypto exchange and comprehensive trading service serving 140 million users in 90 countries. To improve the service quality of Crypto.com, the firm implemented generative AI-powered assistant services on AWS. Modern AI assistants—artificial intelligence systems designed to interact with users through natural language, answer

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Benchmarking Amazon Nova: A comprehensive analysis through MT-Bench and Arena-Hard-Auto

Advanced (300), AI/ML, Amazon Bedrock, Amazon Machine Learning, Amazon Nova, Generative AI, Technical How-to /

Large language models (LLMs) have rapidly evolved, becoming integral to applications ranging from conversational AI to complex reasoning tasks. However, as models grow in size and capability, effectively evaluating their performance has become increasingly challenging. Traditional benchmarking metrics like perplexity and BLEU scores often fail to capture the nuances of real-world interactions, making human-aligned evaluation

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

AI/ML, Amazon Elastic Kubernetes Service, Amazon Machine Learning, Amazon SageMaker HyperPod, Artificial Intelligence, AWS ParallelCluster, Best Practices /

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

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Build an AI-powered automated summarization system with Amazon Bedrock and Amazon Transcribe using Terraform

Amazon Bedrock, Amazon Machine Learning, Amazon Transcribe, Artificial Intelligence, Technical How-to /

Extracting meaningful insights from unstructured data presents significant challenges for many organizations. Meeting recordings, customer interactions, and interviews contain invaluable business intelligence that remains largely inaccessible due to the prohibitive time and resource costs of manual review. Organizations frequently struggle to efficiently capture and use key information from these interactions, resulting in not only productivity

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