Products

Microsoft Windows Server 2019 with SQL Server 2017 Express is a pre-configured Amazon Machine Image (AMI) offered by Amazon Web Services (AWS). This AMI combines the robust capabilities of Windows Server 2019 with the lightweight, free edition of SQL Server 2017 Express, providing a reliable and cost-effective environment for developing, testing, and deploying applications. Designed for seamless integration with AWS services, this AMI simplifies the setup process, allowing users to focus on thei

The "Microsoft Windows Server 2012 RTM with SQL Server Standard 2014" is an Amazon Machine Image (AMI) offered by Amazon Web Services (AWS). This AMI combines the robust capabilities of Windows Server 2012 RTM with the advanced database management features of SQL Server Standard 2014, providing a reliable and scalable environment for deploying enterprise-level applications and services. Key Features and Functionality: - Integrated Environment: Seamless integration between Windows Server 2012 R

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet-21k which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

This is a Object Detection Answering model from TensorFlow Hub

This is a Object Detection Answering model from TensorFlow Hub

This is a Object Detection Answering model from TensorFlow Hub

This is a Object Detection Answering model from PyTorch Hub

This is a Object Detection Answering model from TensorFlow Hub

This is a Image Classification model from PyTorch Hub

This is a Image Classification model from TensorFlow Hub

This is a Extractive Question Answering model from PyTorch Hub

It takes an image as input and returns bounding boxes for the objects in the image. The model is pre-trained on COCO 2017 which comprises images with multiple objects and the task is to identify the objects and their positions in the image. A list of the objects that the model can identify is given at the end of the page. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc

This is a Object Detection Answering model from TensorFlow Hub

This is a Object Detection Answering model from TensorFlow Hub

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 sentences.

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. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

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

The Nitro Enclaves Developer AMI contains the necessary tools and components to build enclave applications. It also contains samples, such as hello-enclave, vsock_sample and kmstool, to demonstrate how to use and develop your own enclave applications.

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet-21k which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.