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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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Accelerate NLP inference with ONNX Runtime on AWS Graviton processors

AWS Machine Learning

ONNX Runtime is the runtime engine used for model inference and training with ONNX. We also demonstrate the resulting speedup through benchmarking. Benchmark setup We used an AWS Graviton3-based c7g.4xl 1014-aws kernel) The ONNX Runtime repo provides inference benchmarking scripts for transformers-based language models.

Benchmark 121
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25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

Bill Dettering is the CEO and Founder of Zingtree , a SaaS solution for building interactive decision trees and agent scripts for contact centers (and many other industries). Interactive agent scripts from Zingtree solve this problem. Agents can also send feedback directly to script authors to further improve processes.

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Achieve ~2x speed-up in LLM inference with Medusa-1 on Amazon SageMaker AI

AWS Machine Learning

We also included a data exploration script to analyze the length of input and output tokens. As a next step, you can explore fine-tuning your own LLM with Medusa heads on your own dataset and benchmark the results for your specific use case, using the provided GitHub repository.

Scripts 75
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Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together

AWS Machine Learning

Current evaluations from Anthropic suggest that the Claude 3 model family outperforms comparable models in math word problem solving (MATH) and multilingual math (MGSM) benchmarks, critical benchmarks used today for LLMs. Media organizations can generate image captions or video scripts automatically.

Benchmark 140
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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

This requirement translates into time and effort investment of trained personnel, who could be support engineers or other technical staff, to review tens of thousands of support cases to arrive at an even distribution of 3,000 per category. Sonnet prediction accuracy through prompt engineering. We expect to release version 4.2.2

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How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

AWS Machine Learning

PrestoDB is an open source SQL query engine that is designed for fast analytic queries against data of any size from multiple sources. For more information on the TPC-H data, its database entities, relationships, and characteristics, refer to TPC Benchmark H. Twilio needed to implement an MLOps pipeline that queried data from PrestoDB.

Scripts 110