AWS Marketplace

Tag: artificial intelligence

Implicit BPR improving recommendations

Improving personalized ranking in recommender systems with Implicit BPR and Amazon SageMaker

A recommender system is an automated software mechanism that uses algorithms and data to personalize product discovery for a particular user. Its essential task is to help users discover the most relevant items within an often-unmanageable set of choices. These days, recommender systems are employed in diverse domains to promote products on e-commerce sites, such […]

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monitoring data in third party models amazon sagemakermodel monitor

Monitoring data quality in third-party models with Amazon SageMaker Model Monitor

Building, training, and deploying machine learning models from scratch can be a time-consuming and costly endeavor for some customers. Moreover, once deployed to production, machine learning models need to be continuously monitored for deviations in model and data quality. To help you expedite model deployment and implement a model monitoring solution, you can integrate pre-trained […]

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Using TorchServe to list PyTorch

Using TorchServe to list PyTorch models at scale in AWS Marketplace

Recently, AWS announced the release of TorchServe, a PyTorch open-source project in collaboration with Facebook. PyTorch is an open-source machine learning framework created by Facebook, which is popular among ML researchers and data scientists. Despite its ease of use and “Pythonic” interface, deploying and managing models in production is still difficult as it requires data scientists to […]

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AI for real time text classification

Building an Artificial Intelligence system for real-time text message classification and learning

by guest author Ievgen Sliusar, Assistant Professor, Ph.D. In this blog post, I  demonstrate how to build an Artificial Intelligence (AI) system for real-time text message classification and learning using Dynamic AI56. This model package is available in AWS Marketplace. With conventional machine learning (ML), you first train your model and then deploy it to […]

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