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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

Using its enterprise software, FloTorch conducted an extensive comparison between Amazon Nova models and OpenAIs GPT-4o models with the Comprehensive Retrieval Augmented Generation (CRAG) benchmark dataset. FloTorch used these queries and their ground truth answers to create a subset benchmark dataset.

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Accelerated PyTorch inference with torch.compile on AWS Graviton processors

AWS Machine Learning

You can see that for the 45 models we benchmarked, there is a 1.35x latency improvement (geomean for the 45 models). You can see that for the 33 models we benchmarked, there is around 2x performance improvement (geomean for the 33 models). We benchmarked 45 models using the scripts from the TorchBench repo.

Benchmark 118
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Fine-tune LLMs with synthetic data for context-based Q&A using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that makes 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. Solution overview The solution comprises two main steps: Generate synthetic data using the Amazon Bedrock InvokeModel API.

APIs 74
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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

These include metrics such as ROUGE or cosine similarity for text similarity, and specific benchmarks for assessing toxicity (Detoxify), prompt stereotyping (cross-entropy loss), or factual knowledge (HELM, LAMA). Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API.

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Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 2: Interactive User Experiences in SageMaker Studio

AWS Machine Learning

SageMaker makes it easy to deploy models into production directly through API calls to the service. It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python. It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python.

Benchmark 127
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Testing times: testingRTC is the smart, synchronized, real-world scenario WebRTC testing solution for the times we live in.

Spearline

And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API. Flip the script With testingRTC, you only need to write scripts once, you can then run them multiple times and scale them up or down as you see fit. Happy days!

Scripts 98
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Image classification model selection using Amazon SageMaker JumpStart

AWS Machine Learning

The former question addresses model selection across model architectures, while the latter question concerns benchmarking trained models against a test dataset. This post provides details on how to implement large-scale Amazon SageMaker benchmarking and model selection tasks. swin-large-patch4-window7-224 195.4M efficientnet-b5 29.0M

APIs 91