AWS SageMaker MLOps
An enterprise cloud-native deployment pipeline demonstrating IAM security compliance, model registry orchestration, and endpoint provisioning within AWS ecosystem constraints.
Executive Summary
Moving machine learning models from local experimentation to production requires rigorous infrastructure management. This architecture outlines a standard enterprise deployment flow within AWS, focusing on secure endpoint creation, model versioning, and strict access controls to ensure compliance with commercial cloud security standards.
Proposed Technical Architecture & Data Foundation
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Model Registry: AWS SageMaker utilized to version and track model artifacts, establishing a clear lineage from training data to production deployment.
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Cloud Infrastructure (IaC): Provisioning of S3 buckets and SageMaker endpoints mapped via Terraform or CloudFormation for repeatable, code-driven deployments.
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Security & Identity: Strict adherence to AWS IAM least-privilege principles, ensuring decoupled execution roles for training jobs and endpoint hosting.
TPM Impact: Governance, Trust, & Continuous Deployment
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Enterprise Scalability: Establishes a foundational pattern for deploying highly available ML endpoints capable of auto-scaling to meet production traffic demands.
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Infrastructure Governance: Translates manual console configurations into auditable IaC, aligning with modern site reliability engineering (SRE) practices.