Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1
GS Caltex connects Amazon SageMaker Canvas to Amazon SageMaker Pipelines so process engineers' no-code models run in production. This post shows how ML engineers reuse validated preprocessing flows and models, cutting deployment from about a week to a few hours and running batch inference every 30 minutes.
GS Caltex, a South Korean energy and chemical company, has implemented MLOps using Amazon SageMaker Canvas and SageMaker Pipelines to streamline the production workflow for their models. Process engineers build and validate models in SageMaker Canvas without writing code, leveraging their process knowledge, while machine learning engineers configure SageMaker Pipelines workflows for deployment and scheduled inference.
This integration has reduced the time required for model deployment from about a week to just a few hours and has significantly lowered inference costs by running batch inference every 30 minutes, instead of continuously running endpoints.
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