Difference between revisions of "Amazon SageMaker"
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↑ https://aws.amazon.com/about-aws/whats-new/2017/11/introducing-amazon-sagemaker/
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| − | + | [[wikipedia:Amazon SageMaker]] ([[AWS timeline|Nov 2017]]) <ref>https://aws.amazon.com/about-aws/whats-new/2017/11/introducing-amazon-sagemaker/</ref> | |
| − | [[ | + | [[Machine learning]] service to create, train, and deploy machine-learning (ML) models in AWS. |
| + | == Example IAM Policies == | ||
| + | <code>[[AmazonSageMakerFeatureStoreAccess]]</code> | ||
| + | { | ||
| + | "Version": "2012-10-17", | ||
| + | "Statement": [ | ||
| + | { | ||
| + | "Effect": "Allow", | ||
| + | "Action": [ | ||
| + | "[[s3:GetObject]]", | ||
| + | "[[s3:PutObject]]", | ||
| + | "[[s3:DeleteObject]]", | ||
| + | "[[s3:ListBucket]]" | ||
| + | ], | ||
| + | "Resource": [ | ||
| + | "arn:aws:s3:::*" | ||
| + | ] | ||
| + | } | ||
| + | ] | ||
| + | } | ||
| + | * [[Amazon SageMaker Canvas]] | ||
| + | |||
| + | == Related == | ||
| + | * [[AmazonSageMakerFullAccess]] | ||
| + | * [[ScheduleConfig]] | ||
| + | * [[Colab]] | ||
| + | |||
| + | == Activities == | ||
| + | * Read https://aws.amazon.com/blogs/machine-learning/train-fraudulent-payment-detection-with-amazon-sagemaker/ | ||
== See also == | == See also == | ||
| − | * {{AWS}} | + | * {{aws sagemaker}} |
| + | * {{AWS ML}} | ||
* {{ML}} | * {{ML}} | ||
| + | |||
| + | [[Category:Computing]] | ||
Latest revision as of 05:26, 24 January 2023
wikipedia:Amazon SageMaker (Nov 2017) [1] Machine learning service to create, train, and deploy machine-learning (ML) models in AWS.
Example IAM Policies[edit]
AmazonSageMakerFeatureStoreAccess
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"s3:PutObject",
"s3:DeleteObject",
"s3:ListBucket"
],
"Resource": [
"arn:aws:s3:::*"
]
}
]
}
Related[edit]
Activities[edit]
See also[edit]
- AWS SageMaker,
aws sagemaker - AWS ML: Amazon SageMaker, Amazon Kendra, AWS Forecast, Amazon Macie, Amazon Textract, Amazon SageMaker Canvas
- Machine learning, Deep learning, AWS Sagemaker, PyTorch, Kubeflow, TensorFlow, Keras, Torch, Spark ML, Tinygrad, Apple Neural Engine, Scikit-learn, MNIST, MLOps, AutoML, ClearML, PostgresML, AWS Batch, Transformer, Diffusion, Backpropagation, JAX, Vector database, LLM, The Forrester Wave: AI/ML Platforms
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