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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. For more details about how to run graph multi-task learning with GraphStorm, refer to Multi-task Learning in GraphStorm in our documentation. introduces refactored graph ML pipeline APIs.

APIs 117
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Pixtral-12B-2409 is now available on Amazon Bedrock Marketplace

AWS Machine Learning

In this post, we walk through how to discover, deploy, and use the Pixtral 12B model for a variety of real-world vision use cases. Performance metrics and benchmarks Pixtral 12B is trained to understand both natural images and documents, achieving 52.5% To begin using Pixtral 12B, choose Deploy.

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Benchmark and optimize endpoint deployment in Amazon SageMaker JumpStart 

AWS Machine Learning

This post explores these relationships via a comprehensive benchmarking of LLMs available in Amazon SageMaker JumpStart, including Llama 2, Falcon, and Mistral variants. We provide theoretical principles on how accelerator specifications impact LLM benchmarking. Additionally, models are fully sharded on the supported instance.

Benchmark 124
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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 94
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Optimizing AI responsiveness: A practical guide to Amazon Bedrock latency-optimized inference

AWS Machine Learning

As businesses increasingly use large language models (LLMs) for these critical tasks and processes, they face a fundamental challenge: how to maintain the quick, responsive performance users expect while delivering the high-quality outputs these sophisticated models promise. In such scenarios, you want to optimize for TTFT.

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

AWS Machine Learning

We gave practical tips, based on hands-on experience with customer use cases, on how to improve text-only RAG solutions, from optimizing the retriever to mitigating and detecting hallucinations. We first introduce routers, and how they can help managing diverse data sources. has 92% accuracy on the HumanEval code benchmark.

APIs 118
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Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing Foundation Models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

Metrics 86