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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

The DS uses SageMaker Training jobs to generate metrics captured by , selects a candidate model, and registers the model version inside the shared model group in their local model registry. You can use the method mlflow.autolog() to log metrics, parameters, and models without the need for explicit log statements.

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

Provide control through transparency of models, guardrails, and costs using metrics, logs, and traces The control pillar of the generative AI framework focuses on observability, cost management, and governance, making sure enterprises can deploy and operate their generative AI solutions securely and efficiently.

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Demystifying machine learning at the edge through real use cases

AWS Machine Learning

Edge is a term that refers to a location, far from the cloud or a big data center, where you have a computer device (edge device) capable of running (edge) applications. Agriculture, mining, surveillance and security, and maritime transportation are some areas where far edge devices play an important role. Edge computing.

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Digital Transformation Challenges: Creating Your Action Plan

aircall

Companies rely heavily on reporting without the advantage of data and analytics that are so critical to establishing efficient, productive workflows. In today’s marketplace, automation, digitization, and big data are your friends. Jobs in transportation and warehousing will increase due to eCommerce growth.

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A 5-Step Checklist for Mastering Enterprise AI

CSM Magazine

Whenever a new iteration of the AI tool is released, remember to monitor key metrics that reflect and reinforce your original goals such as ‘What % of users engage with the assistant?’, ‘What are the most popular topics?’ How many visitors request transfer to a live agent?’ For more information, please visit www.ebi.ai.

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Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

AWS Machine Learning

Amazon Cognito for user authentication with Transport Layer Security (TLS). For logging, we utilize FluentD to push all our container logs to Amazon OpenSearch Service and system metrics to Prometheus. We then use Kibana and the Grafana UI for searching and filtering logs and metrics. Logging and monitoring. Kubeflow Logging.

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

AWS Machine Learning

It serializes these configuration dictionaries (or config dict for short) to their ProtoBuf representation, transports them to the client using gRPC, and then deserializes them back to Python dictionaries. The evaluation takes place on a testing dataset existing only on the server, and the new improved accuracy metrics are produced.

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