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Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

AWS Machine Learning

These SageMaker endpoints are consumed in the Amplify React application through Amazon API Gateway and AWS Lambda functions. To protect the application and APIs from inadvertent access, Amazon Cognito is integrated into Amplify React, API Gateway, and Lambda functions. You access the React application from your computer.

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Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

AWS Machine Learning

We demonstrate how to use the AWS Management Console and Amazon Translate public API to deliver automatic machine batch translation, and analyze the translations between two language pairs: English and Chinese, and English and Spanish. In this post, we present a solution that D2L.ai

APIs 75
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How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost

AWS Machine Learning

A recent initiative is to simplify the difficulty of constructing search expressions by autofilling patent search queries using state-of-the-art text generation models. In this section, we show how to build your own container, deploy your own GPT-2 model, and test with the SageMaker endpoint API. model_fp16.onnx gpt2 and predictor.py

APIs 66
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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

Each business unit has each own set of development (automated model training and building), preproduction (automatic testing), and production (model deployment and serving) accounts to productionize ML use cases, which retrieve data from a centralized or decentralized data lake or data mesh, respectively.

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Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

AWS Machine Learning

We also share the key technical challenges that were solved during construction of the Face-off Probability model. To make an informed decision, we performed a series of benchmarks to verify SageMaker latency and scalability, and validated that average latency was less than 100 milliseconds under the load, which was within our expectations.

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Evaluate large language models for quality and responsibility

AWS Machine Learning

Customers have to leave their development environment to use academic tools and benchmarking sites, which require highly-specialized knowledge. We surveyed existing open-source evaluation frameworks and designed FMEval evaluation API with extensibility in mind.

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Fast-track graph ML with GraphStorm: A new way to solve problems on enterprise-scale graphs

AWS Machine Learning

With GraphStorm, you can build solutions that directly take into account the structure of relationships or interactions between billions of entities, which are inherently embedded in most real-world data, including fraud detection scenarios, recommendations, community detection, and search/retrieval problems.