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@ -727,6 +727,7 @@ _Libraries for Machine Learning. Also see [awesome-machine-learning](https://git
- [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metrics.
- [MindsDB](https://github.com/mindsdb/mindsdb) - MindsDB is an open source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using standard queries.
- [PraisonAI](https://github.com/MervinPraison/PraisonAI) - Production-ready Multi-AI Agents framework with self-reflection, 100+ LLM support, MCP integration, and agentic workflows.
- [RAGFlow](https://github.com/infiniflow/ragflow) - An open-source RAG engine for document understanding and question answering with LLMs.
- [scikit-learn](http://scikit-learn.org/) - The most popular Python library for Machine Learning.
- [Spark ML](http://spark.apache.org/docs/latest/ml-guide.html) - [Apache Spark](http://spark.apache.org/)'s scalable Machine Learning library.
- [Transformers](https://github.com/huggingface/transformers) - A framework that lets you easily use pretrained transformer models for NLP, vision, and audio tasks.