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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

Challenges in data management Traditionally, managing and governing data across multiple systems involved tedious manual processes, custom scripts, and disconnected tools. The diagram shows several accounts and personas as part of the overall infrastructure. The following diagram gives a high-level illustration of the use case.

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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock empowers teams to generate Terraform and CloudFormation scripts that are custom fitted to organizational needs while seamlessly integrating compliance and security best practices. Traditionally, cloud engineers learning IaC would manually sift through documentation and best practices to write compliant IaC scripts.

Scripts 130
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Customized model monitoring for near real-time batch inference with Amazon SageMaker

AWS Machine Learning

Examples include financial systems processing transaction data streams, recommendation engines processing user activity data, and computer vision models processing video frames. A preprocessor script is a capability of SageMaker Model Monitor to preprocess SageMaker endpoint data capture before creating metrics for model quality.

Scripts 110
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Transcribe, translate, and summarize live streams in your browser with AWS AI and generative AI services

AWS Machine Learning

script to automatically copy the cdk configuration parameters to a configuration file by running the following command, still in the /cdk folder: /scripts/postdeploy.sh After the deployment is complete, you have two options. The preferred option is to use the provided postdeploy.sh

APIs 131
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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

script provided with the CRAG benchmark for accuracy evaluations. The script was enhanced to provide proper categorization of correct, incorrect, and missing responses. The default GPT-4o evaluation LLM in the evaluation script was replaced with the mixtral-8x7b-instruct-v0:1 model API.

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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning

For early detection, implement custom testing scripts that run toxicity evaluations on new data and model outputs continuously. Integrating scheduled toxicity assessments and custom testing scripts into your development pipeline helps you continuously monitor and adjust model behavior.

APIs 111