mlops
Learn how to design, develop, deploy and iterate on production-grade ML applications.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Always know what to expect from your data.
Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable,…
Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activelo…
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
The Open Source Feature Store for Machine Learning
ZenML 🙏: The bridge between ML and Ops. https://zenml.io.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
ML pipeline orchestration and model deployments on Kubernetes.
MLOps simplified. One platform, all the functionality you need. Swiss made
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
Open source platform for the machine learning lifecycle
A flexible, high-performance serving system for machine learning models
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Production infrastructure for machine learning at scale
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
INCEpTION provides a semantic annotation platform offering intelligent annotation assistance and knowledge management.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
An open source multi-tool for exploring and publishing data
GPU Sharing Device Plugin for Kubernetes Cluster
A toolkit to run Ray applications on Kubernetes
Kubernetes-friendly ML model management, deployment, and serving.
Algorithms for outlier, adversarial and drift detection
Algorithms for explaining machine learning models