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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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Build and train ML models using a data mesh architecture on AWS: Part 1

AWS Machine Learning

This is mainly targeting the data steward persona, who is responsible for streamlining and standardizing the process of sharing data between data producers and consumers and ensuring compliance with data governance rules. Therefore, data producers from different LoBs are responsible for making data in a consumable form right at the source.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

With SageMaker MLOps tools, teams can easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production. Regulations in the healthcare industry call for especially rigorous data governance.

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Introducing self-service quota management and higher default service quotas for Amazon Textract

AWS Machine Learning

Today, we’re excited to announce self-service quota management support for Amazon Textract via the AWS Service Quotas console, and higher default service quotas in select AWS Regions. Increased default service quotas for Amazon Textract. Synchronous Operations. AnalyzeDocument. US East (Ohio). DetectDocumentText.

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The executive’s guide to generative AI for sustainability

AWS Machine Learning

Organizations are facing ever-increasing requirements for sustainability goals alongside environmental, social, and governance (ESG) practices. Throughout this lifecycle, implementing AWS Well-Architected Framework best practices is recommended. A Gartner, Inc. Figure 7: The generative AI lifecycle 11.

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

AWS Machine Learning

Hear best practices for using unstructured (video, image, PDF), semi-structured (Parquet), and table-formatted (Iceberg) data for training, fine-tuning, checkpointing, and prompt engineering. In this session, learn how to build your first generative AI application with key services such as Amazon Bedrock. 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). The Content Designer Lambda function saves the input in OpenSearch Service in a question’s bank index. Choose Create function.

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