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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

It has applications in areas where data is multi-modal such as ecommerce, where data contains text in the form of metadata as well as images, or in healthcare, where data could contain MRIs or CT scans along with doctor’s notes and diagnoses, to name a few use cases. However, we can use CDE for a wider range of use cases.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

In this scenario, the generative AI application, designed by the consumer, must interact with the fine-tuner backend via APIs to deliver this functionality to the end-users. If an organization has no AI/ML experts in their team, then an API service might be better suited for them. 15K available FM reference Step 1.

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How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

AWS Machine Learning

The AI/ML architecture for EarthSnap is designed around a series of AWS services: Sagemaker Pipeline runs using one of the methods mentioned above (CodeBuild, API, manual) that trains the model and produces artifacts and metrics. The following diagram shows the EarthSnap AI/ML architecture.

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Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning

In this innovation talk, hear how the largest industries, from healthcare and financial services to automotive and media and entertainment, are using generative AI to drive outcomes for their customers. Reserve your seat now! Reserve your seat now! You must bring your laptop to participate. Reserve your seat now!

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Integrate QnABot on AWS with ServiceNow

AWS Machine Learning

The workflow includes the following steps: A QnABot administrator can configure the questions using the Content Designer UI delivered by Amazon API Gateway and Amazon Simple Storage Service (Amazon S3). Amazon Lex V2 getting started- Streaming APIs]([link] Expand the Advanced section and enter the same answer under Markdown Answer.

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning

medium instance to demonstrate deploying LLMs via SageMaker JumpStart, which can be accessed through a SageMaker-generated API endpoint. Instead, adhere to the security best practices in AWS Identity and Access Management (IAM), and create an administrative user and group. We use an ml.t3.medium