Remove 2012 Remove Accountability Remove Feedback
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On Being an Accountable Customer Service Leader

Customer Service Life

Properly authenticating the account. Leaving complete account notes for the next person who interacts with the customer. This exercise reminded me of the time when we started this blog back in 2012. Starting a blog about customer service became instant accountability for me. Quality as accountability.

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Security best practices to consider while fine-tuning models in Amazon Bedrock

AWS Machine Learning

The workflow steps are as follows: The user submits an Amazon Bedrock fine-tuning job within their AWS account, using IAM for resource access. The fine-tuning job initiates a training job in the model deployment accounts. Provide your account, bucket name, and VPC settings. The following code is a sample resource policy.

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25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

Source: Human Resource Management; Issue: 51(4); 2012; Pages 535-548. They don’t do anything else except maybe monitor a few calls and give some feedback. Agents can also send feedback directly to script authors to further improve processes. Feedback loops are imperative to success. To implement continuous training.

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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

The Amazon Bedrock VPC endpoint powered by AWS PrivateLink allows you to establish a private connection between the VPC in your account and the Amazon Bedrock service account. Use the following template to create the infrastructure stack Bedrock-GenAI-Stack in your AWS account. You’re redirected to the IAM console.

APIs 142
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Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning

As you scale your models, projects, and teams, as a best practice we recommend that you adopt a multi-account strategy that provides project and team isolation for ML model development and deployment. Depending on your governance requirements, Data Science & Dev accounts can be merged into a single AWS account.

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Announcing the launch of the model copy feature for Amazon Rekognition Custom Labels

AWS Machine Learning

This feature allows you to copy your Rekognition Custom Labels models across projects, which can be in the same AWS account or across AWS accounts in the same AWS Region, without retraining the models from scratch. In this post, we show you how to copy models between different AWS accounts in the same AWS Region.

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Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

AWS Machine Learning

Your feedback is always welcome; please leave your thoughts and questions in the comments section. Then we build Jupyter notebooks in SageMaker to run the translation process using Amazon Translate public APIs. You can use this solution to improve your translation quality and efficiency.

APIs 96