A Docker image may be tested and ready on a developer workstation, but a local tag such as document-insight-api:1.0.0 is not yet a controlled production artifact. AWS runtimes need a private, durable image location, deployment teams need an immutable identity, and security teams need scanning and audit evidence. This guide pushes the synthetic document-insight-api image to a private Amazon Elastic Container Registry (Amazon ECR) repository. It applies an immutable release tag and...

An AI API that works in a local Python environment is not automatically ready to run as a production container. The image may contain build tools, credentials, stale packages, unnecessary model files, or an application process running as root. It may also lack a reliable health check, graceful shutdown behavior, or enough metadata to identify what was deployed. This guide builds a hardened image for a synthetic document-summarization service named document-insight-api. Docker Desktop is used...

An automated pipeline can build, test, and deploy an AI application within minutes. That speed is valuable, but production releases may still require a person to confirm that the correct change, model configuration, permissions, and infrastructure are being promoted. This guide adds a manual approval gate to an existing AWS CodePipeline workflow for a synthetic application named claims-ai-summary. The pipeline already deploys to staging. After staging succeeds, CodePipeline pauses at...