Products

AWS Entity Resolution helps you more easily match, link, and enhance related customer, product, business, or healthcare records stored across multiple applications, channels, and data stores. You can use flexible and configurable rule, machine learning, or data service provider matching techniques to optimize your records based on your business needs.

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This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub

This is a Extractive Question Answering model from PyTorch Hub

PartyRock makes learning easy with a hands-on, code-free app builder. Experiment with prompt engineering techniques, review generated responses, and develop intuition for generative AI while creating and exploring fun apps.

Windows Server 2019 English Core with Containers is a streamlined version of Microsoft's server operating system, optimized for running containerized applications. This edition provides a minimalistic, command-line interface, reducing resource consumption and enhancing performance, making it ideal for modern cloud-native deployments. Key Features and Functionality: - Container Support: Built-in support for Windows containers, enabling seamless deployment and management of containerized applica

Microsoft Windows Server 2012 R2 RTM with SQL Server 2016 Standard is a robust combination designed to deliver a reliable and scalable server environment. Windows Server 2012 R2 RTM provides a solid foundation for enterprise-level applications, offering enhanced virtualization, storage, networking, and security features. Paired with SQL Server 2016 Standard, this setup enables efficient data management and analytics, supporting real-time operational insights and advanced security measures. This

Microsoft Windows Server 2012 R2 Core is a streamlined, minimalistic installation option of the Windows Server 2012 R2 operating system, designed to provide a low-maintenance, efficient server environment. By installing only essential components, it reduces the servicing and management requirements, minimizes the attack surface, and optimizes resource utilization. This configuration is particularly suitable for organizations seeking to enhance security and performance while maintaining essential

AWS Developer Support offers technical assistance and resources to developers using AWS services, helping them troubleshoot issues and optimize their applications.

This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings.

This is a Sentence Pair Classification model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes a pair of sentences as input and classifies the input pair to 'entailment' or 'no-entailment'. The class label entailment implies the second sentence entails the first sentence, and the no-entailment implies it does not. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of sentenc

Microsoft Windows Server 2019 Base with Containers is a robust operating system designed to support modern application development and deployment. It combines the reliability and performance of Windows Server 2019 with integrated container support, enabling developers and IT professionals to build, manage, and deploy containerized applications efficiently. This integration facilitates the creation of cloud-native applications and the modernization of existing applications using containers and mi

This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings.

This is a Sentence Pair Classification model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes a pair of sentences as input and classifies the input pair to 'entailment' or 'no-entailment'. The class label entailment implies the second sentence entails the first sentence, and the no-entailment implies it does not. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of sentenc

This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub

This is an Image Classification model from PyTorch Hub. It takes an image as input and classifies the image to one of the 1000 classes.