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Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

AWS Machine Learning

In particular, we cover the SMP library’s new simplified user experience that builds on open source PyTorch Fully Sharded Data Parallel (FSDP) APIs, expanded tensor parallel functionality that enables training models with hundreds of billions of parameters, and performance optimizations that reduce model training time and cost by up to 20%.

Scripts 98
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Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

Organizations across industries such as retail, banking, finance, healthcare, manufacturing, and lending often have to deal with vast amounts of unstructured text documents coming from various sources, such as news, blogs, product reviews, customer support channels, and social media. Extract and analyze data from documents.

Scripts 72
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Pre-training genomic language models using AWS HealthOmics and Amazon SageMaker

AWS Machine Learning

In this blog post and open source project , we show you how you can pre-train a genomics language model, HyenaDNA , using your genomic data in the AWS Cloud. Solution overview In this blog post we address pre-training a genomic language model on an assembled genome. Then we deploy that model as a SageMaker real-time inference endpoint.

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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning

Using architecture diagrams as an example, the solution needs to search through reference links and technical documents for architecture diagrams and identify the services present. Therefore, users without ML expertise can enjoy the benefits of a custom labels model through an API call, because a significant amount of overhead is reduced.

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Build a powerful question answering bot with Amazon SageMaker, Amazon OpenSearch Service, Streamlit, and LangChain

AWS Machine Learning

A small number of similar documents (typically three) is added as context along with the user question to the “prompt” provided to another LLM and then that LLM generates an answer to the user question using information provided as context in the prompt. Chunking of knowledge base documents. Implementing the question answering task.

APIs 77
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Introducing an image-to-speech Generative AI application using Amazon SageMaker and Hugging Face

AWS Machine Learning

In our previous blogpost Enable the Visually Impaired to Hear Documents using Amazon Textract and Amazon Polly , we showed you our Text to Speech application called “Read for Me”. In this blog post we walk you through the Solution Architecture behind “Describe For Me”, and the design considerations of our solution.

APIs 90
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Simplify continuous learning of Amazon Comprehend custom models using Comprehend flywheel

AWS Machine Learning

Amazon Comprehend is a managed AI service that uses natural language processing (NLP) with ready-made intelligence to extract insights about the content of documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document.

APIs 68