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Talkdesk for Salesforce provides relief and recovery with industry solutions

Talkdesk

This technology package can reduce friction in the process with an end-to-end customer experience solution that streamlines the administration of PPP loans. Financial institutions can activate the Small Business Lending Solution in as little as 24 hours with some customers doing so in less than a day!

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Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

AWS Machine Learning

Prerequisites Before getting started, make sure you have the following prerequisites: An AWS account. Follow the instructions in Getting Started with the AWS CDK to set up your local environment and bootstrap your development account. We encourage you to deploy the AWS CDK app into your account and build the Generative AI solution.

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Enhance call center efficiency using batch inference for transcript summarization with Amazon Bedrock

AWS Machine Learning

We also explore best practices for optimizing your batch inference workflows on Amazon Bedrock, helping you maximize the value of your data across different use cases and industries. Solution overview The batch inference feature in Amazon Bedrock provides a scalable solution for processing large volumes of data across various domains.

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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

Prerequisites In order to provision ML environments with the AWS CDK, complete the following prerequisites: Have access to an AWS account and permissions within the Region to deploy the necessary resources for different personas. Make sure you have the credentials and permissions to deploy the AWS CDK stack into your account.

Scripts 98
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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

According to a Forbes survey , there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. This walkthrough includes the following prerequisites: An AWS account. Otherwise, your account may hit the service quota limits of running an m5.4x

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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning

Customers can configure an AWS account, the repository, the model, the data used, the pipeline name, the training framework, the number of instances to use for training, the inference framework, and any pre- and post-processing steps and several other configurations to check the model quality, bias, and explainability.

Analytics 130
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Across accounts, automate deployment using export and import dataset, data source, and analysis API calls provided by QuickSight. About the Authors Stephen Randolph is a Senior Partner Solutions Architect at Amazon Web Services (AWS). Ajay Vishwakarma is an ML engineer for the AWS wing of Wipro’s AI solution practice.