Build a custom Q&A dataset using Amazon SageMaker Ground Truth to train a Hugging Face Q&A NLU model

Favorite In recent years, natural language understanding (NLU) has increasingly found business value, fueled by model improvements as well as the scalability and cost-efficiency of cloud-based infrastructure. Specifically, the Transformer deep learning architecture, often implemented in the form of BERT models, has been highly successful, but training, fine-tuning, and optimizing

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Shared by AWS Machine Learning May 6, 2022

Achieve hyperscale performance for model serving using NVIDIA Triton Inference Server on Amazon SageMaker

Favorite Machine learning (ML) applications are complex to deploy and often require multiple ML models to serve a single inference request. A typical request may flow across multiple models with steps like preprocessing, data transformations, model selection logic, model aggregation, and postprocessing. This has led to the evolution of common

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Shared by AWS Machine Learning May 3, 2022

Why warring personalities are crucial for innovation

Favorite Why were the Wright brothers the first to invent the aeroplane? Perhaps because there were two of them, and because they fought all the time. Wilbur and Orville Wright, from wikimedia commons Anyone who is interested in innovation should visit the Basadur Applied Creativity site. There you will find

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Shared by Nick Milton May 3, 2022

Identify paraphrased text with Hugging Face on Amazon SageMaker

Favorite Identifying paraphrased text has business value in many use cases. For example, by identifying sentence paraphrases, a text summarization system could remove redundant information. Another application is to identify plagiarized documents. In this post, we fine-tune a Hugging Face transformer on Amazon SageMaker to identify paraphrased sentence pairs in

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Shared by AWS Machine Learning April 29, 2022