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Accelerate custom generative AI app deployment using pre-built containers with optimized AI models.

Displaying 8 results

The Merlin PyTorch container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with PyTorch, and serve the trained model on Triton Inference Server.
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This container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with TensorFlow.
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The Merlin TensorFlow container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with TensorFlow, and serve the trained model on Triton Inference Server.
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This container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with PyTorch.
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The Merlin HugeCTR container enables you to perform data preprocessing, feature engineering, train models with HugeCTR, and then serve the trained model with Triton Inference Server.
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This container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with HugeCTR.
Container
This container allows users to deploy NVTabular workflows and PyTorch models to Triton Inference server for production.
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This container allows users to deploy NVTabular workflows and TensorFlow models to Triton Inference server for production.
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