Remove Auto-classification Remove BERT Remove Python
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Host concurrent LLMs with LoRAX

AWS Machine Learning Blog

After your requested quotas are applied to your account, you can use the default Studio Python 3 (Data Science) image with an ml.t3.medium Leave default settings for VPC , Subnet , and Auto-assign public IP. Launch templates in Amazon EC2 can be used to deploy multiple instances, with options for load balancing or auto scaling.

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Accelerate hyperparameter grid search for sentiment analysis with BERT models using Weights & Biases, Amazon EKS, and TorchElastic

AWS Machine Learning Blog

Transformer-based language models such as BERT ( Bidirectional Transformers for Language Understanding ) have the ability to capture words or sentences within a bigger context of data, and allow for the classification of the news sentiment given the current state of the world. The code can be found on the GitHub repo. eks-create.sh

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Dialogue-guided visual language processing with Amazon SageMaker JumpStart

AWS Machine Learning Blog

The system is further refined with DistilBERT , optimizing our dialogue-guided multi-class classification process. Additionally, you benefit from advanced features like auto scaling of inference endpoints, enhanced security, and built-in model monitoring. TGI is implemented in Python and uses the PyTorch framework.

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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

In cases where the MME receives many invocation requests, and additional instances (or an auto-scaling policy) are in place, SageMaker routes some requests to other instances in the inference cluster to accommodate for the high traffic. First, a preprocessing model is applied to the input text tokenization (implemented in Python).

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

AWS Machine Learning Blog

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. Use the SageMaker model parallel library The SageMaker model parallel library comes with the SageMaker Python SDK.

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Model hosting patterns in Amazon SageMaker, Part 1: Common design patterns for building ML applications on Amazon SageMaker

AWS Machine Learning Blog

For example, an image classification use case may use three different models to perform the task. The scatter-gather pattern allows you to combine results from inferences run on three different models and pick the most probable classification model. These endpoints are fully managed and support auto scaling.

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Adapting language-based models beyond English

Snorkel AI

For text classification, however, there are many similarities. Snorkel Flow’s “Auto-Suggest Key Terms” feature works on any language with “white-space” tokenization. The following image shows an auto-suggestion from a Spanish Sentiment dataset (“ mucha suerte” translates to “good luck”).

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