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Build a contextual chatbot for financial services using Amazon SageMaker JumpStart, Llama 2 and Amazon OpenSearch Serverless with Vector Engine

AWS Machine Learning

Model choices – SageMaker JumpStart offers a selection of state-of-the-art ML models that consistently rank among the top in industry-recognized HELM benchmarks. We also use Vector Engine for Amazon OpenSearch Serverless (currently in preview) as the vector data store to store embeddings. Lewis et al.

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Learn how Amazon Ads created a generative AI-powered image generation capability using Amazon SageMaker

AWS Machine Learning

Acting as a model hub, JumpStart provided a large selection of foundation models and the team quickly ran their benchmarks on candidate models. Here, Amazon SageMaker Ground Truth allowed ML engineers to easily build the human-in-the-loop workflow (step v). The Amazon API Gateway receives the PUT request (step 1).

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LLM-as-a-judge on Amazon Bedrock Model Evaluation

AWS Machine Learning

Amazon Bedrock , a fully managed service offering high-performing foundation models from leading AI companies through a single API, has recently introduced two significant evaluation capabilities: LLM-as-a-judge under Amazon Bedrock Model Evaluation and RAG evaluation for Amazon Bedrock Knowledge Bases. 0]}-{evaluator_model.split('.')[0]}-{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}"

Metrics 100
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Evaluation of generative AI techniques for clinical report summarization

AWS Machine Learning

This is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API. There are many prompt engineering techniques. It is time-consuming but, at the same time, critical.

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Optimizing AI responsiveness: A practical guide to Amazon Bedrock latency-optimized inference

AWS Machine Learning

Consider benchmarking your user experience to find the best latency for your use case, considering that most humans cant read faster than 225 words per minute and therefore extremely fast response can hinder user experience. In such scenarios, you want to optimize for TTFT. Users prefer accurate responses over quick but less reliable ones.

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Get started with Amazon Titan Text Embeddings V2: A new state-of-the-art embeddings model on Amazon Bedrock

AWS Machine Learning

A common way to select an embedding model (or any model) is to look at public benchmarks; an accepted benchmark for measuring embedding quality is the MTEB leaderboard. The Massive Text Embedding Benchmark (MTEB) evaluates text embedding models across a wide range of tasks and datasets.

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Intelligent healthcare forms analysis with Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. Lastly, the Lambda function stores the question list in Amazon S3.