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Favorite Voice AI is changing the way we use technology, allowing for more natural and intuitive conversations. Meanwhile, advanced AI agents can now understand complex questions and act autonomously on our behalf. In Part 1 of this series, you learned how you can use the combination of Amazon Bedrock and
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Shared by AWS Machine Learning July 11, 2025
Favorite Fraud detection remains a significant challenge in the financial industry, requiring advanced machine learning (ML) techniques to detect fraudulent patterns while maintaining compliance with strict privacy regulations. Traditional ML models often rely on centralized data aggregation, which raises concerns about data security and regulatory constraints. Fraud cost businesses over
Read More Shared by AWS Machine Learning July 11, 2025
Favorite Today, we announce the public preview of long-running execution (asynchronous) flow support within Amazon Bedrock Flows. With Amazon Bedrock Flows, you can link foundation models (FMs), Amazon Bedrock Prompt Management, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, and other AWS services together to build and scale
Read More Shared by AWS Machine Learning July 11, 2025
Favorite Managing access control in enterprise machine learning (ML) environments presents significant challenges, particularly when multiple teams share Amazon SageMaker AI resources within a single Amazon Web Services (AWS) account. Although Amazon SageMaker Studio provides user-level execution roles, this approach becomes unwieldy as organizations scale and team sizes grow. Refer
Read More Shared by AWS Machine Learning July 11, 2025
Favorite What if you could replace hours of data analysis with a minute-long conversation? Large language models can transform how we bridge the gap between business questions and actionable data insights. For most organizations, this gap remains stubbornly wide, with business teams trapped in endless cycles—decoding metric definitions and hunting
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Favorite In Part 1 of this series, we explored how Amazon’s Worldwide Returns & ReCommerce (WWRR) organization built the Returns & ReCommerce Data Assist (RRDA)—a generative AI solution that transforms natural language questions into validated SQL queries using Amazon Bedrock Agents. Although this capability improves data access for technical users,
Read More Shared by AWS Machine Learning July 11, 2025
Favorite Extracting information from unstructured documents at scale is a recurring business task. Common use cases include creating product feature tables from descriptions, extracting metadata from documents, and analyzing legal contracts, customer reviews, news articles, and more. A classic approach to extracting information from text is named entity recognition (NER).
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Favorite This post is co-written with Zhanghao Wu, co-creator of SkyPilot. The rapid advancement of generative AI and foundation models (FMs) has significantly increased computational resource requirements for machine learning (ML) workloads. Modern ML pipelines require efficient systems for distributing workloads across accelerated compute resources, while making sure developer productivity
Read More Shared by AWS Machine Learning July 11, 2025
Favorite This post provides the theoretical foundation and practical insights needed to navigate the complexities of LLM development on Amazon SageMaker AI, helping organizations make optimal choices for their specific use cases, resource constraints, and business objectives. We also address the three fundamental aspects of LLM development: the core lifecycle
Read More Shared by AWS Machine Learning July 11, 2025
Favorite Businesses often face challenges in managing and deriving value from their data. According to McKinsey, 78% of organizations now use AI in at least one business function (as of 2024), showing the growing importance of AI solutions in business. Additionally, 21% of organizations using generative AI have fundamentally redesigned
Read More Shared by AWS Machine Learning July 10, 2025