Remove Auto-classification Remove BERT Remove Generative AI
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How Lumi streamlines loan approvals with Amazon SageMaker AI

AWS Machine Learning Blog

This post explores how Lumi uses Amazon SageMaker AI to meet this goal, enhance their transaction processing and classification capabilities, and ultimately grow their business by providing faster processing of loan applications, more accurate credit decisions, and improved customer experience.

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Amazon EC2 DL2q instance for cost-efficient, high-performance AI inference is now generally available

AWS Machine Learning Blog

DL2q instances are the first instances to bring Qualcomm’s artificial intelligent (AI) technology to the cloud. Model category Number of models Examples​ NLP​ 157 BERT, BART, FasterTransformer, T5, Z-code MOE Generative AI – NLP 40 LLaMA, CodeGen, GPT, OPT, BLOOM, Jais, Luminous, StarCoder, XGen Generative AI – Image 3 Stable diffusion v1.5

BERT 130
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Dialogue-guided visual language processing with Amazon SageMaker JumpStart

AWS Machine Learning Blog

Visual language processing (VLP) is at the forefront of generative AI, driving advancements in multimodal learning that encompasses language intelligence, vision understanding, and processing. Solution overview The proposed VLP solution integrates a suite of state-of-the-art generative AI modules to yield accurate multimodal outputs.

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Deploy thousands of model ensembles with Amazon SageMaker multi-model endpoints on GPU to minimize your hosting costs

AWS Machine Learning Blog

Artificial intelligence (AI) adoption is accelerating across industries and use cases. Recent scientific breakthroughs in deep learning (DL), large language models (LLMs), and generative AI is allowing customers to use advanced state-of-the-art solutions with almost human-like performance.

BERT 95
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Fine-tune GPT-J using an Amazon SageMaker Hugging Face estimator and the model parallel library

AWS Machine Learning Blog

GPT-J is an open-source 6-billion-parameter model released by Eleuther AI. It can support a wide variety of use cases, including text classification, token classification, text generation, question and answering, entity extraction, summarization, sentiment analysis, and many more. 24xlarge, ml.g5.48xlarge, ml.p4d.24xlarge,

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Creating An Information Edge With Conversational Access To Data

Topbots

It not only requires SQL mastery on the part of the annotator, but also more time per example than more general linguistic tasks such as sentiment analysis and text classification. 4] In the open-source camp, initial attempts at solving the Text2SQL puzzle were focussed on auto-encoding models such as BERT, which excel at NLU tasks.[5,

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Fine-tune a BGE embedding model using synthetic data from Amazon Bedrock

AWS Machine Learning Blog

Solution overview BGE stands for Beijing Academy of Artificial Intelligence (BAAI) General Embeddings. It is a family of embedding models with a BERT-like architecture, designed to produce high-quality embeddings from text data. Auto scaling helps make sure the endpoint can handle varying workloads efficiently.