Amazon SageMaker | General Flashcards
What is Amazon SageMaker?
General
Amazon SageMaker | Machine Learning
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models.
What can I do with Amazon SageMaker?
General
Amazon SageMaker | Machine Learning
Amazon SageMaker enables developers and scientists to build machine learning models for use in intelligent, predictive apps.
How do I get started with Amazon SageMaker?
General
Amazon SageMaker | Machine Learning
To get started with Amazon SageMaker, you log into the Amazon SageMaker console, launch a notebook instance with an example notebook, modify it to connect to your data sources, follow the example to build/train/validate models, and deploy the resulting model into production with just a few inputs.
In which regions is Amazon SageMaker available?
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Amazon SageMaker | Machine Learning
For a list of the supported Amazon SageMaker AWS regions, please visit the AWS Region Table for all AWS global infrastructure. Also for more information, see Regions and Endpoints in the AWS General Reference.
Can I get a history of Amazon SageMaker API calls made on my account for security analysis and operational troubleshooting purposes?
General
Amazon SageMaker | Machine Learning
Yes. To receive a history of Amazon SageMaker API calls made on your account, you simply turn on AWS CloudTrail in the AWS Management Console. The following API calls in Amazon SageMaker Runtime are *not* recorded and delivered: InvokeEndpoint.
What is the service availability of Amazon SageMaker?
General
Amazon SageMaker | Machine Learning
Amazon SageMaker is designed for high availability. There are no maintenance windows or scheduled downtimes. Amazon SageMaker APIs run in Amazon’s proven, high-availability data centers, with service stack replication configured across three facilities in each AWS region to provide fault tolerance in the event of a server failure or Availability Zone outage.
What security measures does Amazon SageMaker have?
General
Amazon SageMaker | Machine Learning
Amazon SageMaker ensures that ML model artifacts and other system artifacts are encrypted in transit and at rest. Requests to the Amazon SageMaker API and console are made over a secure (SSL) connection. You pass AWS Identity and Access Management roles to Amazon SageMaker to provide permissions to access resources on your behalf for training and deployment. You can use encrypted S3 buckets for model artifacts and data, as well as pass a KMS key to Amazon SageMaker notebooks, training jobs, and endpoints, to encrypt the attached ML storage volume.
How does Amazon SageMaker secure my code?
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Amazon SageMaker | Machine Learning
Amazon SageMaker stores code in ML storage volumes, secured by security groups and optionally encrypted at rest.
How am I charged for Amazon SageMaker?
General
Amazon SageMaker | Machine Learning
You pay for ML compute, storage, and data processing resources you use for hosting the notebook, training the model, performing predictions, and logging the outputs. Amazon SageMaker allows you to select the number and type of instance used for the hosted notebook, training, and model hosting. You only pay for what you use, as you use it; there are no minimum fees and no upfront commitments.
