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Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions

AWS Machine Learning

SageMaker runs the legacy script inside a processing container. SageMaker takes your script, copies your data from Amazon Simple Storage Service (Amazon S3), and then pulls a processing container. The SageMaker Processing job sets up your processing image using a Docker container entrypoint script.

Scripts 136
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Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

AWS Machine Learning

When you open a notebook in Studio, you are prompted to set up your environment by choosing a SageMaker image, a kernel, an instance type, and, optionally, a lifecycle configuration script that runs on image startup. The main benefit is that a data scientist can choose which script to run to customize the container with new packages.

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Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning

The notebook instance client starts a SageMaker training job that runs a custom script to trigger the instantiation of the Flower client, which deserializes and reads the server configuration, triggers the training job, and sends the parameters response. script and a utils.py The client.py

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Use Amazon SageMaker Studio with a custom file system in Amazon EFS

AWS Machine Learning

The data science team can, for example, use the shared EFS directory to store their Jupyter notebooks, analysis scripts, and other project-related files. He uses his expertise in artificial intelligence and advanced analytics to extract valuable insights and drive meaningful business outcomes for customers.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

AWS Machine Learning

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. In the Spark script, use the system executable command to run pip install , install this library in your local environment, and get the local path of the JAR file dependency.

APIs 86
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Apply fine-grained data access controls with AWS Lake Formation and Amazon EMR from Amazon SageMaker Studio

AWS Machine Learning

Complete the following steps: Download the bootstrap script from s3://emr-data-access-control- /customer-bootstrap-actions/gcsc/replace-rpms.sh , replacing region with your region. We provide the following sample Lifecycle Configuration script to configure the roles: #!/bin/bash SNAPSHOT20221121212949.noarch.rpm. noarch.rpm.

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Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce apps with AI/ML

AWS Machine Learning

Enter the following script in the editor, providing the ARN for the secret you created earlier: #!/bin/bash If you would like, you can take a look at the CodePipeline deploy step, which uses AWS CloudFormation scripts to create SageMaker endpoint and API Gateway with a custom JWT authorizer. Choose Create configuration.

APIs 90