Beyond Unit Tests Build reliable AI products by testing models, prompts, data, workflows, and production behavior together Traditional unit tests are important, but they cannot fully validate an AI-native...
Deploying Foundation Models Fast on AWS How SageMaker JumpStart simplifies model discovery, deployment, customization, and evaluation for production ML workloads Deploying a foundation model can involve model selection, infrastructure...
Scale model inference with demand while keeping GPU, CPU, and endpoint costs under control Machine learning inference costs can rise quickly when endpoints are provisioned for peak traffic but...
Understand the AWS shared responsibility model and build a SOC 2-ready cloud environment without assuming AWS covers everything Moving your application to AWS can reduce a large part of...
Keep AI-assisted development aligned with the original feature goal from requirements through implementation AI coding tools can move quickly from a simple feature request to changes that were never...
A practical troubleshooting guide for file patterns, workspace roots, hook versions, enabled states, and trigger behavior Kiro Agent Hooks can automate tasks when files are created, saved, or deleted,...
A practical guide to writing clear, testable requirements that Kiro can turn into better designs and implementation tasks Kiro uses requirements.md as the starting point for its Spec workflow,...
Move your existing Amazon Q Developer workflow to Kiro with minimal disruption, while preserving your tools, settings, & development habits Migrating from Amazon Q Developer to Kiro is more...
Learn how Kiro credits work, what counts as a Vibe or Spec request, and how to plan your development workflow without wasting credits Kiro’s credit system can be confusing...
A practical guide to reducing unnecessary Kiro usage without slowing down development Kiro can consume credits faster than expected when prompts are broad, tasks are repeatedly refined, or expensive...