Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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Updated
Sep 22, 2024 - Python
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine Learning Engineer certification.
Easy-to-use and flexible AutoML library for Python
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Web tool to automatically mosaic vagina with Deep Neural Network
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Fast and Accurate ML in 3 Lines of Code
Lightning ⚡️ fast forecasting with statistical and econometric models.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Manipulating Python Programs
Client interface for all things Cleanlab Studio
Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.
[ICLR 2022] "Deep AutoAugment" by Yu Zheng, Zhi Zhang, Shen Yan, Mi Zhang
Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
Automated modeling and machine learning framework FEDOT
This repository includes code for the paper "Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection" accepted in AutonomousCyber, ACM CCS, 2024.
This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.
ZenML 🙏: The bridge between ML and Ops. https://zenml.io.
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