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|Machine Learning Toolkit for Kubernetes|
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|Best Practices, code samples, and documentation for Computer Vision.|
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|:rocket: Build and manage real-life data science projects with ease!|
|Fate||5,040||6 hours ago||1||May 06, 2020||734||apache-2.0||Python|
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|Unified Model Serving Framework 🍱|
|Pixie||4,625||17 hours ago||88||April 24, 2021||231||apache-2.0||C++|
|Instant Kubernetes-Native Application Observability|
KServe provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX.
It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. It enables a simple, pluggable, and complete story for Production ML Serving including prediction, pre-processing, post-processing and explainability. KServe is being used across various organizations.
For more details, visit the KServe website.
Since 0.7 KFServing is rebranded to KServe, we still support the RTS release 0.6.x, please refer to corresponding release branch for docs.
To learn more about KServe, how to use various supported features, and how to participate in the KServe community, please follow the KServe website documentation. Additionally, we have compiled a list of presentations and demos to dive through various details.
KServe is an important addon component of Kubeflow, please learn more from the Kubeflow KServe documentation and follow KServe with Kubeflow on AWS to learn how to use KServe on AWS.