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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

By the end of the consulting engagement, the team had implemented the following architecture that effectively addressed the core requirements of the customer team, including: Code Sharing – SageMaker notebooks enable data scientists to experiment and share code with other team members.

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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 93
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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

Applications and services can call the deployed endpoint directly or through a deployed serverless Amazon API Gateway architecture. To learn more about real-time endpoint architectural best practices, refer to Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker.

Scripts 98
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Use Amazon SageMaker Data Wrangler in Amazon SageMaker Studio with a default lifecycle configuration

AWS Machine Learning

Lifecycle configurations (LCCs) are shell scripts to automate customization for your Studio environments, such as installing JupyterLab extensions, preloading datasets, and setting up source code repositories. LCC scripts are triggered by Studio lifecycle events, such as starting a new Studio notebook. Apply the script (see below).

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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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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.