5 Repos
Awesome GitHub RepositoriesModel Inference Functions
Execution of external machine learning models as remote, event-driven tasks for automated labeling.
Distinct from Serverless Function Management: Focuses on ML model inference as a serverless function, distinct from general serverless management.
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Awesome Model Inference Functions GitHub Repositories
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- cvat-ai/cvat
cvat-ai/cvat
15,317Auf GitHub ansehenCVAT is an open-source, web-based platform designed for annotating images, videos, and 3D point clouds to create high-quality training datasets for machine learning. It functions as a containerized server that orchestrates the entire lifecycle of computer vision data, from initial task creation and manual labeling to quality assurance and final dataset export. The platform distinguishes itself through deep integration with machine learning models, allowing users to deploy custom AI models as serverless functions for automated object detection, tracking, and skeleton annotation. It supports co
Executes external machine learning models as remote, event-driven tasks to automate data labeling and object tracking.
Pythonannotationannotation-toolannotations - graviraja/mlops-basics
graviraja/MLOps-Basics
8,585Auf GitHub ansehenMLOps-Basics is a collection of implementation guides and blueprints for automating the machine learning lifecycle. It provides practical workflows for managing the transition of models from training to production deployment, focusing on the integration of operational tools into the machine learning pipeline. The project features specific architectural patterns for deploying containerized models using serverless infrastructure and cloud registries. It includes frameworks for tracking large datasets and model artifacts via remote storage, as well as guides for converting models into standardiz
Deploys containerized machine learning models as scalable, event-driven inference functions via serverless infrastructure.
Jupyter Notebook - cortexlabs/cortex
cortexlabs/cortex
8,013Auf GitHub ansehenCortex is a Kubernetes-based machine learning infrastructure platform designed for deploying, scaling, and managing models and workloads. It functions as a serverless inference engine and GPU cluster orchestrator, providing the tools necessary to execute real-time, asynchronous, and batch model predictions. The platform utilizes declarative infrastructure-as-code for provisioning model clusters and environments. It optimizes operational costs by elastically scaling CPU and GPU resources through the use of spot instances. The system covers a broad set of operational capabilities, including wo
Executes real-time or batch model predictions that scale automatically based on request volume or queue length.
Goinfrastructuremachine-learning - harthur/brain
harthur/brain
7,991Auf GitHub ansehenBrain is a JavaScript library for building, training, and running feed-forward neural networks. It implements a multilayer perceptron model designed for pattern recognition and function approximation. The library includes a standalone inference engine that converts trained models into portable JavaScript functions. This allows predictions to be executed in browser or Node.js environments without requiring the original library dependencies. The system supports persistent model management through JSON serialization for saving and loading network weights. It also provides a streaming mechanism
Generates a portable JavaScript function from a trained network for inference without the original codebase.
JavaScript - nvidia/isaac-gr00t
NVIDIA/Isaac-GR00T
6,222Auf GitHub ansehenBuilds a portable inference engine directly on an RTX PC during installation with fast build times.
Jupyter Notebook
