---
format: "aidr-story-markdown/v1"
id: "91ab97e44683e9c3d93a723bbf8689bd5ccb78a845bf90c1bbdbf663be7446ee"
canonical_url: "https://aidr.today/91ab97e4?lang=en"
title: "Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-11-13T23:22:09.000Z"
category: "Data"
topics: ["amazon","inference","data-engineering"]
source_urls: ["https://aws.amazon.com/blogs/machine-learning/production-mlops-at-gs-caltex-with-amazon-sagemaker-canvas-part-1/"]
summary: "GS Caltex connects Amazon SageMaker Canvas to Amazon SageMaker Pipelines. ML engineers reuse validated preprocessing flows and models. Deployment time is cut from about a week to a few hours. Batch inference runs every 30 minutes."
---

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

> [Open the canonical story](<https://aidr.today/91ab97e4?lang=en>)

**Published:** 2026-11-13T23:22:09.000Z
**Category:** Data
**Topics:** amazon, inference, data\-engineering

## Summary

GS Caltex connects Amazon SageMaker Canvas to Amazon SageMaker Pipelines\. ML engineers reuse validated preprocessing flows and models\. Deployment time is cut from about a week to a few hours\. Batch inference runs every 30 minutes\.

## Sources

- [Story source](<https://aws.amazon.com/blogs/machine-learning/production-mlops-at-gs-caltex-with-amazon-sagemaker-canvas-part-1/>)