Remove 2017 Remove AWS Remove Data Preparation
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A secure approach to generative AI with AWS

AWS Machine Learning Blog

Because their data and model weights are incredibly valuable, customers require them to stay protected, secure, and private, whether that’s from their own administrator’s accounts, their customers, vulnerabilities in software running in their own environments, or even their cloud service provider from having access.

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AWS positioned in the Leaders category in the 2022 IDC MarketScape for APEJ AI Life-Cycle Software Tools and Platforms Vendor Assessment

AWS Machine Learning Blog

The recently published IDC MarketScape: Asia/Pacific (Excluding Japan) AI Life-Cycle Software Tools and Platforms 2022 Vendor Assessment positions AWS in the Leaders category. AWS met the criteria and was evaluated by IDC along with eight other vendors. AWS is positioned in the Leaders category based on current capabilities.

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Deploy large language models for a healthtech use case on Amazon SageMaker

AWS Machine Learning Blog

In this solution, we fine-tune a variety of models on Hugging Face that were pre-trained on medical data and use the BioBERT model, which was pre-trained on the Pubmed dataset and performs the best out of those tried. We implemented the solution using the AWS Cloud Development Kit (AWS CDK).

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3 Major Trends at Strata New York 2017

DataRobot Blog

This highlights the two companies’ shared vision on self-service data discovery with an emphasis on collaboration and data governance. 2) When data becomes information, many (incremental) use cases surface. Interactively explore, combine, and shape diverse datasets into data ready for machine learning and AI applications.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. Around this time, industry observers reported NVIDIA’s strategy pivoting from its traditional gaming and graphics focus to moving into scientific computing and data analytics.

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Embodied AI Chess with Amazon Bedrock

AWS Machine Learning Blog

Solution overview The chess demo uses a broad spectrum of AWS services to create an interactive and engaging gaming experience. The following architecture diagram illustrates the service integration and data flow in the demo. The application’s core backend functionality is handled by a combination of Unit and Pipeline Resolvers.

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