AI · Official
GS Caltex Connects No-Code AI Models to Production Pipelines
Amazon AWS Machine Learning blog ·
At a glance
GS Caltex linked AI models that process engineers built without code to its live production workflow. Splitting the work between two teams cut model deployment time sharply.
Key points
- Process engineers build and check data processing flows and models in SageMaker Canvas.
- ML engineers move these assets into SageMaker Pipelines jobs and schedule deployment.
- Deployment prep dropped from about a week to a few hours, and batch predictions run every 30 minutes.
- GS Caltex said fewer always-on servers (endpoints) lowered its inference costs.
Why it matters
For companies to use AI models in real work, the link between building and running them matters. This article shows concretely how that link is made.
Original source
Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1
Amazon AWS Machine Learning blog · Nov 14, 08:22 KST
Read on Amazon AWS Machine Learning blog ↗AI summarized the original article. Check the original for accuracy. Copyright belongs to Amazon AWS Machine Learning blog.
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