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Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1

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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.

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GS Caltex uses Amazon SageMaker Canvas and Amazon SageMaker Pipelines to connect process engineers’ models to production workflows. The South Korean energy and chemical company divides model development and operations between engineers with complementary skills. Process engineers build and validate models in SageMaker Canvas without writing code, drawing on their process knowledge. Machine learning (ML) engineers configure SageMaker Pipelines workflows for deployment and scheduled inference.…

O overfeed.news indexa e aponta. Publicamos um trecho curto — o artigo completo fica em AWS Machine Learning Blog.

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Production MLOps at GS Caltex with Amazon SageMaker Canvas,…