The fastest path to AI-powered full stack observability, even for lean teams.
Repository Index
47,133 repositories indexed
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Learn how to develop, deploy and iterate on production-grade ML applications.
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
- airflow
- apache
- apache-airflow
- python
🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
- computer-vision
- deep-learning
- image-annotation
- annotation-tool
Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Turns Data and AI algorithms into production-ready web applications in no time.
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
Open-source, self-hosted alerting for metrics, logs, and databases. Includes a built-in AI agent and MCP server.
- monitoring
- time-series
- nightingale
- tsdb
Meshery, the cloud native manager
- meshery
- management-plane
- hacktoberfest
- kubernetes
AI Observability & Evaluation
- llmops
- ai-monitoring
- ai-observability
- llm-eval
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.
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
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
- data-science
- python
- machine-learning
- pycaret
A terminal spreadsheet multitool for discovering and arranging data
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
- data-drift
- jupyter-notebook
- pandas-dataframe
- machine-learning
The Open Source Feature Store for AI/ML
- machine-learning
- features
- ml
- big-data
Data pipelines for cloud config and security data. Build cloud asset inventory, CSPM, FinOps, and vulnerability management solutions. Extract from AWS, Azure, GCP, and 70+ cloud and SaaS sources.