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Awesome GitHub RepositoriesPredictive Model Backends
Fallback serving runtimes for predictive tasks like text classification when generative backends are unavailable.
Distinct from Model Serving: Distinct from Model Serving: focuses on fallback to standard Hugging Face backends for predictive tasks, not general serving optimizations.
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Awesome Predictive Model Backends GitHub Repositories
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- kserve/kserve
kserve/kserve
5,576Ver en GitHubKServe is a Kubernetes-native platform for deploying and serving machine learning models as scalable inference services. It supports both generative AI models, including large language models, and traditional predictive models from frameworks such as TensorFlow, PyTorch, Scikit-Learn, XGBoost, and ONNX. The platform manages the full lifecycle of model deployments, including revision tracking, canary rollouts, A/B testing, and automatic rollbacks, and provides serverless scale-to-zero capabilities for cost-efficient resource management. KServe distinguishes itself through a standardized infere
Provides a fallback Hugging Face backend for predictive tasks when vLLM is not supported.
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