Learn how to develop, deploy and iterate on production-grade ML applications.
Repository Index
47,133 repositories indexed
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Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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 controlling costs and managing access to models and data.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
☁️ Build multimodal AI applications with cloud-native stack
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Machine Learning Engineering Open Book
Turns Data and AI algorithms into production-ready web applications in no time.
The absolute trainer to light up AI agents.
Workflow Engine for Kubernetes
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
An orchestration platform for the development, production, and observation of data assets.
Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort
A curated list of references for MLOps
Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.
Always know what to expect from your data.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
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, and modular.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.
A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems", which is `dmls-book`
Build, Manage and Deploy AI/ML Systems