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

AWS Machine Learning

Usually, if the dataset or model is too large to be trained on a single instance, distributed training allows for multiple instances within a cluster to be used and distribute either data or model partitions across those instances during the training process. We use a VPC peering configuration within the Region in this example.

Scripts 73
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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

We provide examples demonstrating experiment tracking and using the model registry with MLflow from SageMaker training jobs and Studio, respectively, in the provided notebook. The MLflow Python SDK provides a convenient way to log metrics, runs, and artifacts, and it interfaces with the API resources hosted under the namespace /api/.

APIs 73
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Buddying Up – Putting Virtual Employee Assistants at the Heart of Agent Development

TechSee

For example, Amazon’s Alexa for Business helps employees delegate tasks, while Nokia’s MIKA helps agents find answers as they perform complicated tasks or diagnose problems. For example, VEAs might combine visual customer assistance with agent decision support tools, motivation and career guidance. Gamification.

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Achieve enterprise-grade monitoring for your Amazon SageMaker models using Fiddler

AWS Machine Learning

Some examples are also available on the GitHub repo. Ensure your model has data capture enabled. On the SageMaker console, navigate to your model’s serving endpoint and ensure you have enabled data capture into an Amazon Simple Storage Service (Amazon S3) bucket. Now upload your training dataset.

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Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas

AWS Machine Learning

Exploring, analyzing, interpreting, and finding trends in data is essential for businesses to achieve successful outcomes. Business analysts play a pivotal role in facilitating data-driven business decisions through activities such as the visualization of business metrics and the prediction of future events.