Learn how to develop, deploy and iterate on production-grade ML applications.
Repository Index
47,133 repositories indexed
1–24 of 49 · Most stars
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
- airflow
- apache
- apache-airflow
- python
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/
- neural-network
- search-engine
- knn-algorithm
- hnsw
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.
- machine-learning
- ai
- ml
- mlflow
Label Studio is a multi-type data labeling and annotation tool with standardized output format
- computer-vision
- deep-learning
- image-annotation
- annotation-tool
☁️ Build multimodal AI applications with cloud-native stack
- neural-search
- cloud-native
- deep-learning
- machine-learning
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
- mlops
- interpretability
- explainability
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
- workflow
- kubernetes
- argo
- dag
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.
- search-engine
- semantic-search
- semantic-search-engine
- vector-search
An orchestration platform for the development, production, and observation of data assets.
- data-pipelines
- dagster
- workflow
- data-science
Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort
- machine-learning
- mlops
- model-deployment
- model-monitoring
A curated list of references for MLOps
- machine-learning
- mlops
- data-science
- engineering
Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.
- llm
- llmops
- model-inference
- fine-tuning
Always know what to expect from your data.
- pipeline-tests
- dataquality
- datacleaning
- datacleaner
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
- machine-learning
- experiment-track
- deep-learning
- keras
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.
- kedro
- hacktoberfest
- mlops
- python
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.
- cloud-computing
- deep-learning
- gpu
- hyperparameter-tuning
A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems", which is `dmls-book`
- data-science
- machine-learning-production
- mlops
Build, Manage and Deploy AI/ML Systems
- machine-learning
- model-management
- ai
- ml