From the course: Leveraging Cloud-Based Machine Learning on AWS: Real-World Applications

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SageMaker deploy

SageMaker deploy - Amazon Web Services (AWS) Tutorial

From the course: Leveraging Cloud-Based Machine Learning on AWS: Real-World Applications

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SageMaker deploy

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- [Instructor] Deploying your SageMaker model into production is fairly simple. Ultimately SageMaker is able to provide you with one-click deployment or the ability to automate the deployment of your model into production. So keep in mind that the automation is pre-built, and that we don't have to do a lot to prepare the model to go into production, and they're going to provide you with step-by-step instructions, wizards somewhat, with the ability to get that model sized, get the appropriate CPU attached, get the appropriate storage to cached, and get it into production and get it working for you. After it's deployed the ability to manage it as a hosted environment and the ability to have those things scale, and the ability to leverage the elastic capabilities of the cloud-based system that it's on, in this case Amazon Web Services, are provided for you as well. So keep in mind that machine learning systems can be…

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