April 13, 2024, 11:18 p.m. | Atsushi Suzuki

DEV Community dev.to

Previously, I used AWS SageMaker Studio for model training in my work. However, when I received a generous $10,000 credit from Google Cloud for Startups, I decided to transition our training environment to Vertex AI Workbench.


This article explores the usability differences between SageMaker and Vertex AI and documents our migration process.





Building the Model Training Environment





Creating the Dockerfile


In SageMaker, application code was not included in the container image. Instead, we used dependencies to load external code and …

ai article aws aws sagemaker cloud credit differences environment gcp google google cloud googlecloud however machinelearning sagemaker startups studio training transition usability vertex work

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Data Science Analyst

@ Mayo Clinic | AZ, United States

Sr. Data Scientist (Network Engineering)

@ SpaceX | Redmond, WA