Free MLOps course from DataTalks.Club
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Updated
Apr 11, 2024 - Jupyter Notebook
Free MLOps course from DataTalks.Club
The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
Simple and Distributed Machine Learning
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, FEDML Nexus AI (https://fedml.ai) is your generative AI platform at scale.
Boosting your Web Services of Deep Learning Applications.
RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry
Model Deployment at Scale on Kubernetes 🦄️
Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
Serverless Machine Learning Course for building AI-enabled Prediction Services from models and features
nndeploy是一款模型端到端部署框架。以多端推理以及基于有向无环图模型部署为基础,致力为用户提供跨平台、简单易用、高性能的模型部署体验。
A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
Starter app for fastai v3 model deployment on Render
A Beautiful Flask Web API for Yolov7 (and custom) models
Fast model deployment on any cloud 🚀
Transform your pythonic research to an artifact that engineers can deploy easily.
🤖 An automated machine learning framework for audio, text, image, video, or .CSV files (50+ featurizers and 15+ model trainers). Python 3.6 required.
BentoML Example Projects 🎨
Serving PyTorch models with TorchServe 🔥
Deploy DL/ ML inference pipelines with minimal extra code.
The official python package for NimbleBox. Exposes all APIs as CLIs and contains modules to make ML 🌸
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