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

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In short

The enterprise workflow allows machine learning engineers to review, approve, and register visual assets directly from Amazon SageMaker Canvas into the Amazon SageMaker Model Registry. Amazon EventBridge subsequently initiates automated pipelines every 30 minutes to manage data intake, preprocessing, and model inference via Amazon S3. The scheduled batch approach eliminates idle endpoints to curtail infrastructure costs while preserving exact data preparation flows. Engineers actively oversee pipeline execution history and system performance using Amazon CloudWatch logs.

Key points

  • Amazon SageMaker Pipelines reuses the original data preparation flows and assets built in…: Amazon SageMaker Pipelines reuses the original data preparation flows and assets built in SageMaker Canvas.
  • Machine learning engineers manage production paths and register approved models within th…: Machine learning engineers manage production paths and register approved models within the Amazon SageMaker Model Registry.
  • Amazon EventBridge and Amazon CloudWatch handle pipeline execution triggers and operation…: Amazon EventBridge and Amazon CloudWatch handle pipeline execution triggers and operational performance monitoring.

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

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 meaningfully lowered its inference costs.

This post explains how GS Caltex connects SageMaker Canvas to SageMaker Pipelines…

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