An asynchronous engine for continuous & autonomous machine learning, built for real-time usage.
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Intuitive convenience tooling for lightning-fast, efficient development and ensuring quality in LLM-based applications.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Mistral supports AI agents, automated workflows, orchestration, or delegated tasks.
Deep learning foundations and applications.
C, C++, and Python tools for named entity recognition and relation extraction.
MITRE ATLAS™ provides machine-learning models, research, training resources, or evaluation tools.
A Julia package for fitting (statistical) mixed-effects models.
Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts.
A Repository Containing Classification, Clustering, Regression, Recommender Notebooks with illustration to make them.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
All-in-one web-based IDE specialized for machine learning and data science.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
ML-Ops provides machine-learning models, research, training resources, or evaluation tools.
Machine learning and numerical analysis tools for Node.js and the Browser!
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
500+ ML/AI interview Q&A with runnable code — covers ML fundamentals, deep learning, NLP, PyTorch, scikit-learn pipelines, and system design.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Friendly machine learning for the web!
A set of functions to support the development of machine learning algorithms.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
MLDB provides machine-learning models, research, training resources, or evaluation tools.
Version and deploy your ML models following GitOps principles.
A high performance, memory efficient, maximally parallelized ensemble learning, integrated with scikit-learn.