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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

Finance 122
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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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Avaya Shakes Off Debt, Heading to Public Market

Fonolo

Banking giant ING recently switched from an Avaya call center to a system built internally using Twilio APIs. Understanding Industry Benchmarks. Five9 finished a major revamp of their platform, adopting the new approach of “microservices”. See Five9 Preps for Digital Era. Start at the 9 minute mark in this video for more.

Marketing 173
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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

In addition, they use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide an answer to the user’s request. In Part 1, we focus on creating accurate and reliable agents.

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Deploy Amazon SageMaker Autopilot models to serverless inference endpoints

AWS Machine Learning

In this post, we use the UCI Bank Marketing dataset to predict if a client will subscribe to a term deposit offered by the bank. To launch an Autopilot job using the SageMaker Boto3 libraries, we use the create_auto_ml_job API. To learn more about Autopilot training modes, refer to Training modes. Solution overview.

Banking 83
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Illustrative notebooks in Amazon SageMaker JumpStart

AWS Machine Learning

They show the usage of various SageMaker and JumpStart APIs. This notebook demonstrates how to deploy AlexaTM 20B through the JumpStart API and run inference. Nowadays, several ML methods have strong social implications, for example they are used to predict bank loans, insurance rates, or advertising.

APIs 107
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Top 4 Reasons for Live Chat Popularity

Comm100

Imagine if your bank only provided information over the phone – it would be?in Live chat takes care of this by seamlessly integrating into any technology stack with its flexible API. quick, convenient, and fits your need for instant communication when you can’t (or don’t want to) pick up the phone. in trouble very quickly?as

CRM 67