Amazon Q Developer

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Build a scalable containerized web application on AWS using the MERN stack with Amazon Q Developer – Part 1

Amazon Cognito, Amazon DocumentDB, Amazon Elastic Container Service, Amazon Q Developer, Technical How-to /

The MERN (MongoDB, Express, React, Node.js) stack is a popular JavaScript web development framework. The combination of technologies is well-suited for building scalable, modern web applications, especially those requiring real-time updates and dynamic user interfaces. Amazon Q Developer is a generative AI-powered assistant that improves developer efficiency across the different phases of the software development […]

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Whiteboard to cloud in minutes using Amazon Q, Amazon Bedrock Data Automation, and Model Context Protocol

Amazon API Gateway, Amazon Bedrock Data Automation, Amazon Q, Amazon Q Developer /

Upgrading legacy systems has become increasingly important to stay competitive in today’s market as outdated infrastructure can cost organizations time, money, and market position. However, modernization efforts face challenges like time-consuming architecture reviews, complex migrations, and fragmented systems. These delays not only impact engineering teams but have broader impacts including lost market opportunities, reduced competitiveness,

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Building AIOps with Amazon Q Developer CLI and MCP Server

Advanced (300), Amazon Q Developer, Generative AI, Technical How-to /

IT teams face mounting challenges as they manage increasingly complex infrastructure and applications, often spending countless hours manually identifying operational issues, troubleshooting problems, and performing repetitive maintenance tasks. This operational burden diverts valuable technical resources from innovation and strategic initiatives. Artificial intelligence for IT operations (AIOps) presents a transformative solution, using AI to automate operational

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Containerize legacy Spring Boot application using Amazon Q Developer CLI and MCP server

AI/ML, Amazon Elastic Kubernetes Service, Amazon Q Developer, Generative AI, Java, Self-serve Migration /

Organizations can optimize their migration and modernization projects by streamlining the containerization process for legacy applications. With the right tools and approaches, teams can transform traditional applications into containerized solutions efficiently, reducing the time spent on manual coding, testing, and debugging while enhancing developer productivity and accelerating time-to-market. During containerization initiatives, organizations can address compatibility,

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Streamline deep learning environments with Amazon Q Developer and MCP

Amazon Q, Amazon Q Developer, Artificial Intelligence, AWS Deep Learning AMIs /

Data science teams working with artificial intelligence and machine learning (AI/ML) face a growing challenge as models become more complex. While Amazon Deep Learning Containers (DLCs) offer robust baseline environments out-of-the-box, customizing them for specific projects often requires significant time and expertise. In this post, we explore how to use Amazon Q Developer and Model

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Accelerating data science innovation: How Bayer Crop Science used AWS AI/ML services to build their next-generation MLOps service

Amazon API Gateway, Amazon EventBridge, Amazon Q, Amazon Q Business, Amazon Q Developer, Amazon SageMaker, Amazon SageMaker Data & AI Governance, Artificial Intelligence, Customer Solutions, Generative AI /

The world’s population is expanding at a rapid rate. The growing global population requires innovative solutions to produce food, fiber, and fuel, while restoring natural resources like soil and water and addressing climate change. Bayer Crop Science estimates farmers need to increase crop production by 50% by 2050 to meet these demands. To support their

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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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