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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. Based on customer feedback for the experimental APIs we released in GraphStorm 0.2, introduces refactored graph ML pipeline APIs. Specifically, GraphStorm 0.3 In addition, GraphStorm 0.3

APIs 100
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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 115
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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 128
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Unlocking complex problem-solving with multi-agent collaboration on Amazon Bedrock

AWS Machine Learning

For example, in the case of travel planning, the agent would need to maintain a high-level plan for checking weather forecasts, searching for hotel rooms and attractions, while simultaneously reasoning about the correct usage of a set of hotel-searching APIs. We refer to this approach as assertion-based benchmarking.

Finance 91
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A review of purpose-built accelerators for financial services

AWS Machine Learning

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. The CUDA API and SDK were first released by NVIDIA in 2007. GPU PBAs, 4% other PBAs, 4% FPGA, and 0.5%

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Best practices to build generative AI applications on AWS

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, Mistral AI, Stability AI, and Amazon via a single API. 2022) introduced an idea of zero-shot CoT by using FMs’ untapped zero-shot capabilities. Kojima et al.

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Using LLMs to fortify cyber defenses: Sophos’s insight on strategies for using LLMs with Amazon Bedrock and Amazon SageMaker

AWS Machine Learning

Figure 1: Confusion matrix for the five-severity-level classification using Anthropic Claude 3 Sonnet The performance observed in this benchmark task indicates this is a particularly hard problem for an unmodified, all-purpose LLM, and the problem requires a more specialized model, specifically trained or fine-tuned on cybersecurity data.

APIs 80