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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 97
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Contact Center Trends 2021: The CX Watershed

Fonolo

A properly scripted menu leads customers to the answers they need, provides them with the opportunity to navigate to a live agent, and decreases the overall call volume that reaches the call center. Seven hundred twenty-two million smartphones were shipped in 2012, bringing the worldwide installed base to 1 billion. Emotion Detection.

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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 70
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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

You can use this script add_users_and_groups.py After running the script, if you check the Amazon Cognito user pool on the Amazon Cognito console, you should see the three users created. import boto3 # Session using the SageMaker Execution Role in the Data Science Account session = boto3.Session() large', framework_version='1.0-1',

APIs 69
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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.