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Deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK

AWS Machine Learning

Model data is stored on Amazon Simple Storage Service (Amazon S3) in the JumpStart account. The web application interacts with the models via Amazon API Gateway and AWS Lambda functions as shown in the following diagram. Prerequisites You must have the following prerequisites: An AWS account The AWS CLI v2 Python 3.6

APIs 92
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Highlight text as it’s being spoken using Amazon Polly

AWS Machine Learning

The text can be input from the browser or through an API call to the endpoint exposed by our solution. This calls an API (3) through Amazon API Gateway , to invoke an AWS Lambda function (4). To create this solution in your account, follow the instructions in the README.md

APIs 67
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Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

One important aspect of this foundation is to organize their AWS environment following a multi-account strategy. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs.

APIs 70
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The Future of Debt Collection Agencies: Contact Center Technology and Customer-Centric Strategies

NobelBiz

Account Setup and Verification : Upon receiving a debt, the agency sets up an account for the debtor and verifies all the details. Call centers are equipped with tools that allow agents to quickly access a debtor’s full account information, ensuring that every interaction is informed and constructive.

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3 Quick Principles of Re-engineering a Process in Salesforce

Aria Solutions

Account for the existing security and access, and the possible need to change them. Demo early, demo often. This is not to say we haven’t gone through system testing, sprint demos and UAT with customers, deployed to production, and still had to come back and make adjustments during the support period.

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Quick Principles of Re-engineering a Process in Salesforce

Aria Solutions

Account for the existing security and access, and the possible need to change them. Demo early, demo often. This is not to say we haven’t gone through system testing, sprint demos and UAT with customers, deployed to production, and still had to come back and make adjustments during the support period.

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Automatically generate impressions from findings in radiology reports using generative AI on AWS

AWS Machine Learning

Prerequisites To get started, you need an AWS account in which you can use SageMaker Studio. In order to run inference through SageMaker API, make sure to pass the Predictor class. You will need to create a user profile for SageMaker Studio if you don’t already have one. The training instance type used in this post is ml.p3.16xlarge.