Real-Time MLOps on AWS: Metadata-Driven CI/CD with Blue/Green Deployments
Deploy and update real-time ML endpoints safely—using a declarative metadata file, automated CI/CD, and canary rollouts with rollback protections.
About us
We are passionate about the public cloud as well as the DevOps culture and practices!
We believe that the cloud is the new normal and we assist businesses to adopt the public cloud and DevOps practices.
Why?
Real-time ML endpoints are rarely “deploy once and forget.” Models change frequently—new training runs, optimizations, fine-tuned variants, or entirely new foundation models. The operational risk is not the update itself; it is shipping the update safely, repeatably, and with rollback across the full inference path (API → preprocessing → SageMaker).
This whitepaper shows a practical, AWS-native pattern for metadata-driven CI/CD and blue/green releases that reduce downtime, limit blast radius, and standardize delivery across many models and teams.
Download the whitepaper: Real-Time MLOps on AWS: Metadata-Driven CI/CD with Blue/Green Deployments
A production-ready reference architecture for deploying and updating Amazon SageMaker real-time inference endpoints using a metadata-driven CI/CD pipeline. Each model is managed in its own repository with a declarative metadata file that drives builds, infrastructure provisioning, canary traffic shifting, and automated rollback—supporting both custom container models and Hugging Face prebuilt models.
What You’ll Learn?
• How to implement metadata-driven model deployment, where a single version-controlled file defines model type, artifacts, and runtime configuration.
• How to build an AWS-native CI/CD pipeline that provisions SageMaker endpoints and deploys API + Lambda components consistently.
• How to apply blue/green + canary deployments using AWS CodeDeploy for the Lambda preprocessing layer, with CloudWatch alarms for rollback.
• How to manage multiple models at organizational scale (repo-per-model, multi-model endpoints where appropriate, and centralized routing patterns).
• How to reduce operational risk and cost via automated cleanup of previous stacks/endpoints after successful rollout.
• Practical security and operations guidance: least-privilege roles, encrypted artifacts, logging posture, and monitoring strategy.
Key AWS Services Covered
Amazon SageMaker
Hosts the real-time inference endpoints and exposes model performance and resource metrics for scaling and health checks.
Amazon API Gateway
Provides the public/private API front door with authentication, throttling, validation, and optional response caching.
AWS Lambda
Runs lightweight preprocessing/postprocessing logic to keep SageMaker focused on inference and simplify request handling.
AWS CodeDeploy
Performs blue/green and canary traffic shifting for Lambda versions with automated rollback based on alarms.
AWS CodePipeline
Orchestrates the end-to-end CI/CD workflow from source changes to deployed infrastructure and code.
AWS CloudFormation
Provisions and updates AWS infrastructure (including SageMaker resources) in a repeatable, auditable way.
Ready to Customize AWS Control Tower at Scale?
AWS gives you the building blocks for production-grade ML serving—but a metadata-driven CI/CD framework gives you the discipline to ship model changes safely at scale. Whether you are operating real-time inference for a fast-scaling product team or a compliance-heavy enterprise, this approach standardizes how models are built, deployed, and rolled out across repositories and teams, while enforcing security, observability, and rollback-by-default releases.
With a declarative, GitOps-aligned workflow and automated blue/green traffic shifting, you gain consistent deployments and auditability—without slowing iteration on models.
Book a meeting
Ready to unlock more value from your cloud? Whether you're exploring a migration, optimizing costs, or building with AI—we're here to help. Book a free consultation with our team and let's find the right solution for your goals.