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GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning

adds new APIs to customize GraphStorm pipelines: you now only need 12 lines of code to implement a custom node classification training loop. To help you get started with the new API, we have published two Jupyter notebook examples: one for node classification, and one for a link prediction task. Specifically, GraphStorm 0.3

APIs 116
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From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

AWS Machine Learning

For a qualitative question like “What caused inflation in 2023?”, However, for a quantitative question such as “What was the average inflation in 2023?”, For instance, instead of saying “What caused inflation in 2023?”, the user could disambiguate by asking “What caused inflation in 2023 according to analysts?”,

APIs 111
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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.

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Build a RAG-based QnA application using Llama3 models from SageMaker JumpStart

AWS Machine Learning

On Hugging Face, the Massive Text Embedding Benchmark (MTEB) is provided as a leaderboard for diverse text embedding tasks. It currently provides 129 benchmarking datasets across 8 different tasks on 113 languages. medium instance to demonstrate deploying the model as an API endpoint using an SDK through SageMaker JumpStart.

APIs 127
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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. Having a unified developer experience when accessing custom models or base models through Amazon Bedrock’s API. Ease of deployment through a fully managed, serverless, service. 2, 3, 3.1,

APIs 139
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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

In September of 2023, we announced the launch of Amazon Titan Text Embeddings V1, a multilingual text embeddings model that converts text inputs like single words, phrases, or large documents into high-dimensional numerical vector representations. In this benchmark, 33 different text embedding models were evaluated on the MTEB tasks.

Benchmark 126
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Learn how Amazon Ads created a generative AI-powered image generation capability using Amazon SageMaker

AWS Machine Learning

In a March 2023 survey, Amazon Ads found that among advertisers who were unable to build successful campaigns, nearly 75 percent cited building the creative content as one of their biggest challenges. Acting as a model hub, JumpStart provided a large selection of foundation models and the team quickly ran their benchmarks on candidate models.