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Anthropic Claude 3.5 Sonnet ranks number 1 for business and finance in S&P AI Benchmarks by Kensho

AWS Machine Learning

Sonnet currently ranks at the top of S&P AI Benchmarks by Kensho , which assesses large language models (LLMs) for finance and business. For example, there could be leakage of benchmark datasets’ questions and answers into training data. Anthropic Claude 3.5 Kensho is the AI Innovation Hub for S&P Global. Anthropic Claude 3.5

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Five Ways to Keep Your CX Program Alive and Well in 2019

Beyond Philosophy

However, in our global Customer Experience consultancy, we encourage moving away from segmenting based on how much they spend with you and instead grouping centered on other variables. For example, if you have a customer that interacts with you online, and only online, work with those preferences when you are developing the account.

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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.

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The ROI of Contact Center Benchmarking

Transparent BPO

And one of the best ways to measure improvement in the contact center, according to Jeff Rumburg, co-founder and managing partner at MetricNet , a contact center benchmarking and consulting firm, and the subject of this month’s expert interview series , is through the practice of benchmarking. Benchmarking and ROI.

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Q&A recap | 2022 SaaS retention benchmarks

ChurnZero

In our webinar, 2022 SaaS retention benchmarks , SaaS Capital Manager Director Rob Belcher shares the results from their 11th annual B2B SaaS benchmarking survey. You can download the full report for net retention and gross retention benchmarks as well as retention metrics in relation to ACV, growth, size, and more.

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Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0

AWS Machine Learning

This sets a new benchmark for state-of-the-art performance in critical medical diagnostic tasks, from identifying cancerous cells to detecting genetic abnormalities in tumors. Prerequisites We assume you have access to and are authenticated in an AWS account. The AWS CloudFormation template for this solution uses t3.medium

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LLM continuous self-instruct fine-tuning framework powered by a compound AI system on Amazon SageMaker

AWS Machine Learning

There are various techniques of preference alignment, including proximal policy optimization (PPO), direct preference optimization (DPO), odds ratio policy optimization (ORPO), group relative policy optimization (GRPO), and other algorithms, that can be used in this process. Set up a SageMaker notebook instance.