Favorite The rise of contextual and semantic search has made ecommerce and retail businesses search straightforward for its consumers. Search engines and recommendation systems powered by generative AI can improve the product search experience exponentially by understanding natural language queries and returning more accurate results. This enhances the overall user

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Shared by AWS Machine Learning April 4, 2024

Favorite Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. SageMaker Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity.

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Shared by AWS Machine Learning April 4, 2024

Favorite Amazon SageMaker Canvas allows you to use machine learning (ML) to generate predictions without having to write any code. It does so by covering the end-to-end ML workflow: whether you’re looking for powerful data preparation and AutoML, managed endpoint deployment, simplified MLOps capabilities, or the ability to configure foundation

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Shared by AWS Machine Learning April 3, 2024

Favorite This is a guest post co-written with Michael Feil at Gradient. Evaluating the performance of large language models (LLMs) is an important step of the pre-training and fine-tuning process before deployment. The faster and more frequent you’re able to validate performance, the higher the chances you’ll be able to

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Shared by AWS Machine Learning April 3, 2024

Favorite In 2023, AWS announced an expanded collaboration with Hugging Face to accelerate our customers’ generative artificial intelligence (AI) journey. Hugging Face, founded in 2016, is the premier AI platform with over 500,000 open source models and more than 100,000 datasets. Over the past year, we have partnered to make

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Shared by AWS Machine Learning April 3, 2024

Favorite The generative artificial intelligence (AI) revolution is in full swing, and customers of all sizes and across industries are taking advantage of this transformative technology to reshape their businesses. From reimagining workflows to make them more intuitive and easier to enhancing decision-making processes through rapid information synthesis, generative AI

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Shared by AWS Machine Learning April 3, 2024

Favorite This is a guest post co-written with Meta’s PyTorch team and is a continuation of Part 1 of this series, where we demonstrate the performance and ease of running PyTorch 2.0 on AWS. Machine learning (ML) research has proven that large language models (LLMs) trained with significantly large datasets

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Shared by AWS Machine Learning April 2, 2024

Favorite Amazon Lex provides advanced conversational artificial intelligence (AI) capabilities to enable self-service support for your organization’s contact center. With Amazon Lex, you can implement an omnichannel strategy where customers engage via phone, websites, and messaging platforms. The bots can answer FAQs, provide self-service experiences, or triage customer requests before transferring

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Shared by AWS Machine Learning March 30, 2024

Favorite Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl, C4, Wikipedia, and ArXiv. These datasets encompass a broad range of topics and domains. Although the resulting models yield amazingly good

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Shared by AWS Machine Learning March 29, 2024

Favorite Today, customers of all industries—whether it’s financial services, healthcare and life sciences, travel and hospitality, media and entertainment, telecommunications, software as a service (SaaS), and even proprietary model providers—are using large language models (LLMs) to build applications like question and answering (QnA) chatbots, search engines, and knowledge bases. These

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Shared by AWS Machine Learning March 29, 2024