xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现

Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes

Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

A python library for decision tree visualization and model interpretation.

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

Deep Learning Server and CLI for Torch and TensorRT

A collection of research papers on decision, classification and regression trees with implementations.

Distributed AI Model Training and LLM Fine-Tuning on Kubernetes

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

Provide an input CSV and a target field to predict, generate a model + code to run it.

[UNMAINTAINED] Automated machine learning for analytics & production

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

The missing bridge between your ML models and your AI agents.

Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.

An AI-powered multi-model threat detection platform for identifying phishing emails, malicious URLs, Smishing SMS and Network Intrusion Detection.