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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

By completing these steps, the new Amazon Bedrock chat app should be accessible on the Google Chat console for the persons or groups that you authorized in your Google Workspace. He enjoys supporting customers in their digital transformation journey, using big data, machine learning, and generative AI to help solve their business challenges.

APIs 125
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Build a custom UI for Amazon Q Business

AWS Machine Learning

It also enables conversing with Amazon Q through an interface personalized to your use case. For instructions, refer to How do I integrate IAM Identity Center with an Amazon Cognito user pool and the associated demo video. VPCId – The ID of the existing VPC that can be used to deploy the demo.

APIs 139
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Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

AWS Machine Learning

The policies are defined in a central location, allowing multiple analytics and ML services, such as AWS Glue, Amazon Athena , and SageMaker, to interact with data stored in Amazon S3. The dataset also includes sensitive information like personal phone numbers. For GlueDatabaseName , enter demo. print(classified_map).

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Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

AWS Machine Learning

Yaoqi Zhang is a Senior Big Data Engineer at Mission Cloud. Adrian Martin is a Big Data/Machine Learning Lead Engineer at Mission Cloud. . ## Translate - with custom terminology import boto3 import json # Initialize a session of Amazon Translate translate=boto3.client('translate')

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2021: TechSee’s Year in Review

TechSee

product or service demos , and. Looking back on a year in review, we’ve made four new patent submissions beyond the seven current patents we hold in the CX technologies, Visual Computing, Augmented Reality, and Big Data space. We attended seven events this year, five of which were in-person! document signing.

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How to Bring Agile Innovation to Customer Success

Totango

Improved customer collaboration follows from prioritizing individuals and interactions, a result of success teams engaging closely with customers and customer data. Responsiveness to change results from allowing customer data to drive CS plan implementation. Delivering more customized, personalized CS outcomes.

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AI-based call center: How do they work?

NobelBiz

From improving response times to personalizing interactions, artificial intelligence is now setting new standards in customer service efficiency and effectiveness. This evolution has been driven by advancements in machine learning, natural language processing, and big data analytics. of interactions that are automated using AI.