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Transforming credit decisions using generative AI with Rich Data Co and AWS

AWS Machine Learning

They provide access to external data and APIs or enable specific actions and computation. To improve accuracy, we tested model fine-tuning, training the model on common queries and context (such as database schemas and their definitions). Before joining RDC, he served as a Lead Data Scientist at KPMG, advising clients globally.

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Building an efficient MLOps platform with OSS tools on Amazon ECS with AWS Fargate

AWS Machine Learning

Moreover, it provides a straightforward way to track data lineage, so we can foresee which datasets will be affected by newly introduced changes. The following figure shows schema definition and model which reference it. Environment variables : Set environment variables, such as model paths, API keys, and other necessary parameters.

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Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

AWS Machine Learning

Generative AI models have the potential to revolutionize enterprise operations, but businesses must carefully consider how to harness their power while overcoming challenges such as safeguarding data and ensuring the quality of AI-generated content. It uses the Vector Search index and performs a semantic search on the vector data store.

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Design secure generative AI application workflows with Amazon Verified Permissions and Amazon Bedrock Agents

AWS Machine Learning

Agents automatically call the necessary APIs to interact with the company systems and processes to fulfill the request. The App calls the Claims API Gateway API to run the claims proxy passing user requests and tokens. Claims API Gateway runs the Custom Authorizer to validate the access token.

APIs 88
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Intelligent document processing with AWS AI and Analytics services in the insurance industry: Part 2

AWS Machine Learning

Before you get started, refer to Part 1 for a high-level overview of the insurance use case with IDP and details about the data capture and classification stages. In Part 1, we saw how to use Amazon Textract APIs to extract information like forms and tables from documents, and how to analyze invoices and identity documents.

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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

In this post, we address these limitations by implementing the access control outside of the MLflow server and offloading authentication and authorization tasks to Amazon API Gateway , where we implement fine-grained access control mechanisms at the resource level using Identity and Access Management (IAM). Adds an IAM authorizer.

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How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

When the message is received by the SQS queue, it triggers the AWS Lambda function to make an API call to the Amp catalog service. The Lambda function retrieves the desired show metadata, filters the metadata, and then sends the output metadata to Amazon Kinesis Data Streams. Data Engineer for Amp on Amazon.