GS Caltex Integrates Canvas Models into Production MLOps
Understand this story
GS Caltex connects models that process engineers build and validate no-code in Amazon SageMaker Canvas to SageMaker Pipelines production workflows, cutting the time ML engineers need to deploy models from about a week to a few hours. The AWS blog says the approach reuses validated preprocessing pipelines and models. The operating conditions given in the AWS blog are: the full reference implementation is in a GitHub repository; after creating the pipeline, run it manually to validate before attaching a scheduled trigger; before testing the deployment, first disable the Amazon EventBridge rule and remove its target.
AI overview based on source reports · Updated 14 hr ago
Coverage timeline
Follow the coverage to explore different perspectives.
- AWS Machine Learning BlogProduction MLOps at GS Caltex with Amazon SageMaker Canvas, part 1
GS Caltex connects models built by process engineers in Amazon SageMaker Canvas to production workflows in Amazon SageMaker Pipelines, with ML engineers handling deployment and scheduled inference. The company automated the handoff so ML engineers now deploy models within a few hours instead of about a week, and it runs batch inference every 30 minutes, which it reports has meaningfully lowered inference costs. The post details the reference architecture, including reusing Data Wrangler preprocessing flows, registering and approving models in Model Registry, and scheduling runs with Amazon EventBridge.
Activity for this story
- Current comparable activity
- 7
- Peak comparable activity
- 10Nov 14 08:00
- Change in the past 24 hours
- –
Trends compare the same fully observed participants, which may cover fewer participants than current activity. Move the pointer or tap the chart to inspect hourly activity; use the left and right arrow keys with a keyboard.