Remove 2012 Remove Best practices Remove Scripts
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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

Policy 3 – Attach AWSLambda_FullAccess , which is an AWS managed policy that grants full access to Lambda, Lambda console features, and other related AWS services.

Scripts 130
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Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions

AWS Machine Learning

The best practice for migration is to refactor these legacy codes using the Amazon SageMaker API or the SageMaker Python SDK. 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.

Scripts 143
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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

For an example account structure to follow organizational unit best practices to host models using SageMaker endpoints across accounts, refer to MLOps Workload Orchestrator. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices. Prerequisites.

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The Case For the Anti-Script: A Multifactor Analysis of Script Adherence

Balto

“The anti-script doesn’t mean that you should wing it on every call… what anti-script means is, think about a physical paper script and an agent who is reading it off word for word… you’re taking the most powerful part of the human out of the human.” Share on Twitter. Share on Facebook.

Scripts 52
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Schedule your notebooks from any JupyterLab environment using the Amazon SageMaker JupyterLab extension

AWS Machine Learning

Migrating from interactive development on notebooks to batch jobs required you to copy code snippets from the notebook into a script, package the script with all its dependencies into a container, and schedule the container to run. In the following section, we show an example of using initialization scripts to install packages.

Scripts 100
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Integrate HyperPod clusters with Active Directory for seamless multi-user login

AWS Machine Learning

To achieve this multi-user environment, you can take advantage of Linux’s user and group mechanism and statically create multiple users on each instance through lifecycle scripts. Create a HyperPod cluster with an SSSD-enabled lifecycle script Next, you create a HyperPod cluster with LDAPS/Active Directory integration.

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Operationalize your Amazon SageMaker Studio notebooks as scheduled notebook jobs

AWS Machine Learning

In addition to the interactive ML experience, data workers also seek solutions to run notebooks as ephemeral jobs without the need to refactor code as Python modules or learn DevOps tools and best practices to automate their deployment infrastructure. Ensure that you have validated this selection. Schedule your job.