GS Caltex uses Amazon SageMaker to implement production MLOps.
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GS Caltex uses Amazon SageMaker Canvas and Pipelines to connect process engineers' models to production workflows. Process engineers build and validate models without code in SageMaker Canvas, while ML engineers configure SageMaker Pipelines workflows for deployment and scheduling inference. ML engineers transfer SageMaker Canvas assets to Pipelines in just a few hours, with Pipelines managing model versions and execution history, enabling batch inference every 30 minutes and reducing inference costs.
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- AWS Machine Learning BlogGS Caltex uses Amazon SageMaker Canvas and Amazon SageMaker Pipelines to connect process engineer models to production workflows.
GS Caltex enables process engineers to build and validate models without code through Amazon SageMaker Canvas, and then configure deployment and scheduled inference using Amazon SageMaker Pipelines. ML engineers can transfer SageMaker Canvas assets to Pipelines in just a few hours, with model version deployment and execution history managed by Pipelines, achieving batch processing of inference every 30 minutes and reducing inference costs.
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