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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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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 136
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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.

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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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Get to production-grade data faster by using new built-in interfaces with Amazon SageMaker Ground Truth Plus

AWS Machine Learning

With this new capability, multiple Ground Truth Plus users can now create a new project and batch , share data, and receive data using the same AWS account through self-serve interfaces. Before you get started, make sure you have the following prerequisites: An AWS account. Request a new project. Set up a project team. Create a batch.

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Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

AWS Machine Learning

get('Account') identity = boto3.client('sts').get_caller_identity()['Arn'] pip install retrying from urllib.request import urlretrieve import json import os import boto3 import random import time from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth, RequestError credentials = boto3.Session().get_credentials()

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