4 Repos
Awesome GitHub RepositoriesModel Serving Transformations
Processes that convert trained models into active network endpoints for predictions.
Distinguishing note: No candidates cover the actual transformation of a model file into a serving application; others focus on the model architecture or the endpoint itself.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Model Serving Transformations. Refine with filters or upvote what's useful.
Awesome Model Serving Transformations GitHub Repositories
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- flyteorg/flyte
flyteorg/flyte
7,095Auf GitHub ansehenFlyte is a Kubernetes-based machine learning orchestrator and containerized pipeline manager designed for coordinating AI workflows and data pipelines. It functions as an engine for defining and executing resilient pipelines, utilizing a data lineage tracker to maintain immutable execution states and ensure reproducible outputs. The platform distinguishes itself by packaging individual tasks into separate containers to ensure dependency isolation and environment consistency. It provides specialized capabilities for machine learning, including the transformation of trained models into scalable
Transforms trained models into active endpoints that serve predictions to users or external applications.
Go - kserve/kserve
kserve/kserve
5,576Auf GitHub ansehenKServe 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
Transforms input data and model output through pluggable pipelines for feature engineering and data preparation.
Go - seldonio/seldon-core
SeldonIO/seldon-core
4,752Auf GitHub ansehenSeldon Core ist ein auf Kubernetes basierender Server für Machine-Learning-Modelle und ein MLOps-Inference-Framework. Es fungiert als Serving-Engine für mehrere Modelle und als Pipeline-Orchestrator, der Modelle als skalierbare Microservices verpackt, die über standardisierte REST- und gRPC-APIs bereitgestellt werden. Das Projekt zeichnet sich durch graphbasierte Inference-Pipelines aus, die Modelle und Datentransformatoren zu sequenziellen Workflows verketten. Es optimiert die Hardwareauslastung durch Shared-Serving für mehrere Modelle und Strategien für dynamisches Memory-Overcommit, während es gleichzeitig Produktionsexperimente durch gewichtetes Traffic-Routing, A/B-Tests und Shadow-Deployments unterstützt. Das Framework deckt ein breites Spektrum an MLOps-Funktionen ab, darunter bedarfsgesteuertes Autoscaling, asynchrone Request-Verarbeitung über Message-Busse sowie umfassendes Monitoring für Data Drift, Ausreißer und die Erklärbarkeit von Vorhersagen. Es bietet zudem Infrastrukturmanagement für die Konfiguration der Modell-Runtime und sichere Kommunikation mittels TLS-Verschlüsselung über Control- und Data-Planes hinweg.
Provides pluggable pipelines to transform input data and model outputs for feature engineering and preparation.
Goaiopsdeploymentkubernetes - fizyr/keras-retinanet
fizyr/keras-retinanet
4,388Auf GitHub ansehenThis project is a deep learning implementation of the RetinaNet architecture for detecting and classifying objects within images. Built as a Keras object detection framework and a TensorFlow computer vision tool, it provides a complete neural network implementation based on the RetinaNet paper. The framework includes specialized components such as a Feature Pyramid Network and a focal loss function to handle object detection. It features a configurable backbone architecture and anchor-based bounding boxes to predict object locations across varying scales and aspect ratios. The toolset covers
Ships post-inference transformations that decode raw tensor outputs into human-readable bounding boxes.
Python
