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> | |
− | [[wikipedia:Amazon SageMaker]] | + | [[Machine learning]] service to create, train, and deploy machine-learning (ML) models in AWS. |
− | [[Machine learning]] service | ||
+ | == 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]] | [[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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