Structured data response with Amazon Bedrock: Prompt Engineering and Tool Use

Favorite Generative AI is revolutionizing industries by streamlining operations and enabling innovation. While textual chat interactions with GenAI remain popular, real-world applications often depend on structured data for APIs, databases, data-driven workloads, and rich user interfaces. Structured data can also enhance conversational AI, enabling more reliable and actionable outputs. A

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Shared by AWS Machine Learning June 26, 2025

Amazon Bedrock Agents observability using Arize AI

Favorite This post is cowritten with John Gilhuly from Arize AI. With Amazon Bedrock Agents, you can build and configure autonomous agents in your application. An agent helps your end-users complete actions based on organization data and user input. Agents orchestrate interactions between foundation models (FMs), data sources, software applications,

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Shared by AWS Machine Learning June 25, 2025

No-code data preparation for time series forecasting using Amazon SageMaker Canvas

Favorite Time series forecasting helps businesses predict future trends based on historical data patterns, whether it’s for sales projections, inventory management, or demand forecasting. Traditional approaches require extensive knowledge of statistical methods and data science methods to process raw time series data. Amazon SageMaker Canvas offers no-code solutions that simplify

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Shared by AWS Machine Learning June 24, 2025

Accelerate foundation model training and inference with Amazon SageMaker HyperPod and Amazon SageMaker Studio

Favorite Modern generative AI model providers require unprecedented computational scale, with pre-training often involving thousands of accelerators running continuously for days, and sometimes months. Foundation Models (FMs) demand distributed training clusters — coordinated groups of accelerated compute instances, using frameworks like PyTorch — to parallelize workloads across hundreds of accelerators

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Shared by AWS Machine Learning June 21, 2025