Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1 | Amazon Web Services
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.
Moving assets from SageMaker Canvas into SageMaker Pipelines still required integration work. ML engineers had to set data paths for production, supply the model configuration, and connect preprocessing and inference jobs. GS Caltex automated this handoff while reusing the assets created in SageMaker Canvas.
GS Caltex reports that its ML engineers now deploy models within a few hours, compared with about a week before. The company runs batch inference every 30 minutes, reducing the need for continuously running endpoints. GS Caltex reports that this has...
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