{
  "id": 19884645,
  "title": "Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1",
  "url": "https://urgent.news/2026/11/13/production-mlops-at-gs-caltex-with-amazon-sagemaker-canvas-part-1",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-13T23:22:09.000Z",
  "source": {
    "name": "AWS Machine Learning",
    "slug": "aws-machine-learning",
    "url": "https://aws.amazon.com/blogs/machine-learning/production-mlops-at-gs-caltex-with-amazon-sagemaker-canvas-part-1/"
  },
  "original_language": "en",
  "account": null,
  "summary": "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.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}