Remove 2012 Remove Metrics 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 119
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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

Upon a new model version registration, someone with the authority to approve the model based on the metrics should approve or reject the model. The model artifact is created in the shared services account Amazon Simple Storage Service (Amazon S3) bucket. s3:GetObject', 's3:GetObjectVersion' ], #read 'Resource': 'arn:aws:s3::: /*' }] }. 's3:GetObject',

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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

Examples of such use cases include scaling up a feature engineering job that was previously tested on a small sample dataset on a small notebook instance, running nightly reports to gain insights into business metrics, and retraining ML models on a schedule as new data becomes available.

Scripts 100
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Deep demand forecasting with Amazon SageMaker

AWS Machine Learning

The input data is a multi-variate time series that includes hourly electricity consumption of 321 users from 2012–2014. Amazon Forecast is a time-series forecasting service based on machine learning (ML) and built for business metrics analysis. For HPO, we use the RRSE as the evaluation metric for all the three algorithms.

Metrics 98
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Scale ML workflows with Amazon SageMaker Studio and Amazon SageMaker HyperPod

AWS Machine Learning

You can now use SageMaker Studio to discover the SageMaker HyperPod clusters, and view cluster details and metrics. Use the following script to create the domain and replace the export variables accordingly. Also attach the following JSON policy to the role, which enables SageMaker Studio to access the SageMaker HyperPod cluster.

Scripts 101
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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

Scripts 98