ASML Moves On-Premises Machine Learning to Google Cloud to Speed Chip Manufacturing Models
ASML· NetherlandsBigQuery · Google Kubernetes Engine · Cloud Build +1ASML, the Dutch photolithography machine maker, migrated its on-premises machine learning platform that predicts process performance per device layer to Google Cloud, using BigQuery, Kubernetes Engine, and Cloud Build with partners Rackspace and ML6. The move cut product release cycles from monthly to biweekly, improved overall engineering efficiency including time to market by up to 40%, sped up data queries 25X with BigQuery, saved each engineer about four hours per day previously spent on data parsing, and reduced encryption/build/test time from hours to about 10 minutes.
Overview
ASML, the Dutch photolithography machine maker, migrated its on-premises machine learning platform that predicts process performance per device layer to Google Cloud, using BigQuery, Kubernetes Engine, and Cloud Build with partners Rackspace and ML6. The move cut product release cycles from monthly to biweekly, improved overall engineering efficiency including time to market by up to 40%, sped up data queries 25X with BigQuery, saved each engineer about four hours per day previously spent on data parsing, and reduced encryption/build/test time from hours to about 10 minutes.
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Inspect the highlighted sourceThe challenge
ASML's on-premises machine learning solution, which predicts process performance per device layer and must retrain itself as processes change, worked but could not adapt fast enough to the growth in data, model and software build complexity in an industry where everything is measured in seconds.
The solution
ASML partnered with Google Cloud Partner Rackspace to extend its secure environment into the cloud, and with machine learning specialist ML6 to train staff and optimize its data ingestion and model training pipelines, dedicating a BigQuery and Kubernetes cluster with auto-scaling to data ingestion for its machine learning product that predicts process performance per device layer.
Sources
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