AWS Details GS Caltex MLOps Pipeline: Model Deployment Cut from a Week to Hours
AWS ML Blog · rss · 2026-11-14
AWS ML Blog's tutorial shows how GS Caltex connects no-code SageMaker Canvas models built by process engineers to SageMaker Pipelines managed by ML engineers, cutting deployment time from a week to a few hours and running batch inference every 30 minutes to lower costs. Covers the reference architecture (S3 + Model Registry + EventBridge), pitfalls like stale .flow URIs, pinning container images, preserving learned preprocessing parameters, and handling missing target columns at inference. Reference code is on GitHub.
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