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Learn how to 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,…
Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.
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
ZenML 🙏: One AI Platform from Pipelines to Agents. 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-stop AI delivery platform, all the features you need.
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while control…
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
