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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

In this post, we build a secure enterprise application using AWS Amplify that invokes an Amazon SageMaker JumpStart foundation model, Amazon SageMaker endpoints, and Amazon OpenSearch Service to explain how to create text-to-text or text-to-image and Retrieval Augmented Generation (RAG). You may need to request a quota increase.

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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 123
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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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Run your local machine learning code as Amazon SageMaker Training jobs with minimal code changes

AWS Machine Learning

In this post, we show you how to use this new capability to run local ML code as a SageMaker Training job. Solution overview You can now run your ML code written in your IDE or notebook as a SageMaker Training job by annotating the function, which acts as an entry point to the user’s code base, with a simple decorator.

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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 84
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Build an agronomic data platform with Amazon SageMaker geospatial capabilities

AWS Machine Learning

Key decisions include what crops to plant, how much fertilizer to apply, how to control pests, and when to harvest. Priyanka Mahankali is a Guidance Solutions Architect at AWS for more than 5 years building cross-industry solutions including technology for global agriculture customers.

APIs 85