May 2025

Deploy Amazon SageMaker Projects with Terraform Cloud

Amazon SageMaker, Amazon SageMaker Data & AI Governance, Amazon SageMaker Studio, Technical How-to /

Amazon SageMaker Projects empower data scientists to self-serve Amazon Web Services (AWS) tooling and infrastructure to organize all entities of the machine learning (ML) lifecycle, and further enable organizations to standardize and constrain the resources available to their data science teams in pre-packaged templates. For AWS customers using Terraform to define and manage their infrastructure-as-code (IaC), […]

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How ZURU improved the accuracy of floor plan generation by 109% using Amazon Bedrock and Amazon SageMaker

Amazon Bedrock, Amazon SageMaker, Customer Solutions /

ZURU Tech is on a mission to change the way we build, from town houses and hospitals to office towers, schools, apartment blocks, and more. Dreamcatcher is a user-friendly platform developed by ZURU that allows users with any level of experience to collaborate in the building design and construction process. With the simple click of

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Going beyond AI assistants: Examples from Amazon.com reinventing industries with generative AI

Amazon Bedrock, Amazon Bedrock Agents, Amazon Machine Learning, Artificial Intelligence, Generative AI, Retail /

Generative AI revolutionizes business operations through various applications, including conversational assistants such as Amazon’s Rufus and Amazon Seller Assistant. Additionally, some of the most impactful generative AI applications operate autonomously behind the scenes, an essential capability that empowers enterprises to transform their operations, data processing, and content creation at scale. These non-conversational implementations, often in

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Architect a mature generative AI foundation on AWS

Best Practices, Generative AI, Intermediate (200), Thought Leadership /

Generative AI applications seem simple—invoke a foundation model (FM) with the right context to generate a response. In reality, it’s a much more complex system involving workflows that invoke FMs, tools, and APIs and that use domain-specific data to ground responses with patterns such as Retrieval Augmented Generation (RAG) and workflows involving agents. Safety controls

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Using Amazon OpenSearch ML connector APIs

Amazon Comprehend, Amazon OpenSearch Service, Amazon Titan, Intermediate (200), Technical How-to /

When ingesting data into Amazon OpenSearch, customers often need to augment data before putting it into their indexes. For instance, you might be ingesting log files with an IP address and want to get a geographic location for the IP address, or you might be ingesting customer comments and want to identify the language they

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Bridging the gap between development and production: Seamless model lifecycle management with Amazon Bedrock

Amazon Bedrock, Generative AI, Intermediate (200), Technical How-to /

In the landscape of generative AI, organizations are increasingly adopting a structured approach to deploy their AI applications, mirroring traditional software development practices. This approach typically involves separate development and production environments, each with its own AWS account, to create logical separation, enhance security, and streamline workflows. Amazon Bedrock is a fully managed service that

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Revolutionizing earth observation with geospatial foundation models on AWS

Advanced (300), Amazon OpenSearch Service, Amazon SageMaker, Amazon SageMaker AI, Foundation models, Geospatial ML with Amazon SageMaker, Technical How-to /

Emerging transformer-based vision models for geospatial data—also called geospatial foundation models (GeoFMs)—offer a new and powerful technology for mapping the earth’s surface at a continental scale, providing stakeholders with the tooling to detect and monitor surface-level ecosystem conditions such as forest degradation, natural disaster impact, crop yield, and many others. GeoFMs represent an emerging research

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Create an agentic RAG application for advanced knowledge discovery with LlamaIndex, and Mistral in Amazon Bedrock

Advanced (300), Amazon Bedrock, Amazon Bedrock Knowledge Bases, Technical How-to /

Agentic Retrieval Augmented Generation (RAG) applications represent an advanced approach in AI that integrates foundation models (FMs) with external knowledge retrieval and autonomous agent capabilities. These systems dynamically access and process information, break down complex tasks, use external tools, apply reasoning, and adapt to various contexts. They go beyond simple question answering by performing multi-step

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Text-to-image basics with Amazon Nova Canvas

Amazon Nova, Best Practices, Foundational (100), Generative AI, Python, Technical How-to /

AI image generation has emerged as one of the most transformative technologies in recent years, revolutionizing how you create and interact with visual content. Amazon Nova Canvas is a generative model in the suite of Amazon Nova creative models that enables you to generate realistic and creative images from plain text descriptions. This post serves

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Part 3: Building an AI-powered assistant for investment research with multi-agent collaboration in Amazon Bedrock and Amazon Bedrock Data Automation

Advanced (300), Amazon Bedrock, Amazon Bedrock Agents, Financial Services /

In the financial services industry, analysts need to switch between structured data (such as time-series pricing information), unstructured text (such as SEC filings and analyst reports), and audio/visual content (earnings calls and presentations). Each format requires different analytical approaches and specialized tools, creating workflow inefficiencies. Add on top of this the intense time pressure resulting

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