Technical How-to

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Build AWS architecture diagrams using Amazon Q CLI and MCP

AI/ML, Amazon Q Developer, Best Practices, Generative AI, Technical How-to /

Creating professional AWS architecture diagrams is a fundamental task for solutions architects, developers, and technical teams. These diagrams serve as essential communication tools for stakeholders, documentation of compliance requirements, and blueprints for implementation teams. However, traditional diagramming approaches present several challenges: Time-consuming process – Creating detailed architecture diagrams manually can take hours or even days […]

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AWS costs estimation using Amazon Q CLI and AWS Cost Analysis MCP

Amazon Q Developer, Best Practices, Generative AI, Technical How-to /

Managing and optimizing AWS infrastructure costs is a critical challenge for organizations of all sizes. Traditional cost analysis approaches often involve the following: Complex spreadsheets – Creating and maintaining detailed cost models, which requires significant effort Multiple tools – Switching between the AWS Pricing Calculator, AWS Cost Explorer, and third-party tools Specialized knowledge – Understanding

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Tailor responsible AI with new safeguard tiers in Amazon Bedrock Guardrails

Amazon Bedrock, Amazon Bedrock Guardrails, Artificial Intelligence, Best Practices, Generative AI, Responsible AI, Technical How-to, Thought Leadership /

Amazon Bedrock Guardrails provides configurable safeguards to help build trusted generative AI applications at scale. It provides organizations with integrated safety and privacy safeguards that work across multiple foundation models (FMs), including models available in Amazon Bedrock, as well as models hosted outside Amazon Bedrock from other model providers and cloud providers. With the standalone

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

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

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 key challenge is that LLMs (Large

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Using Amazon SageMaker AI Random Cut Forest for NASA’s Blue Origin spacecraft sensor data

Aerospace & Satellite, Amazon SageMaker, Amazon SageMaker AI, Amazon Simple Storage Service (S3), Amazon VPC, Artificial Intelligence, Intermediate (200), Python, Technical How-to /

The successful deorbit, descent, and landing of spacecraft on the Moon requires precise control and monitoring of vehicle dynamics. Anomaly detection provides a unique utility for identifying important states that might represent vehicle behaviors of interest. By producing unique vehicle behavior points, critical spacecraft system states can be identified to be more appropriately addressed and

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Build an intelligent multi-agent business expert using Amazon Bedrock

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

In this post, we demonstrate how to build a multi-agent system using multi-agent collaboration in Amazon Bedrock Agents to solve complex business questions in the biopharmaceutical industry. We show how specialized agents in research and development (R&D), legal, and finance domains can work together to provide comprehensive business insights by analyzing data from multiple sources.

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

Amazon SageMaker, Amazon SageMaker Data Wrangler, Intermediate (200), Technical How-to /

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 data wrangling, making time series forecasting

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Meeting summarization and action item extraction with Amazon Nova

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

Meetings play a crucial role in decision-making, project coordination, and collaboration, and remote meetings are common across many organizations. However, capturing and structuring key takeaways from these conversations is often inefficient and inconsistent. Manually summarizing meetings or extracting action items requires significant effort and is prone to omissions or misinterpretations. Large language models (LLMs) offer

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Building a custom text-to-SQL agent using Amazon Bedrock and Converse API

Amazon Bedrock, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon DynamoDB, Amazon Machine Learning, AWS Lambda, Technical How-to /

Developing robust text-to-SQL capabilities is a critical challenge in the field of natural language processing (NLP) and database management. The complexity of NLP and database management increases in this field, particularly while dealing with complex queries and database structures. In this post, we introduce a straightforward but powerful solution with accompanying code to text-to-SQL using

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Accelerate threat modeling with generative AI

Amazon Bedrock, Artificial Intelligence, Generative AI, Security, Technical How-to /

In this post, we explore how generative AI can revolutionize threat modeling practices by automating vulnerability identification, generating comprehensive attack scenarios, and providing contextual mitigation strategies. Unlike previous automation attempts that struggled with the creative and contextual aspects of threat analysis, generative AI overcomes these limitations through its ability to understand complex system relationships, reason

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