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
May 30, 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.
A SapientML plugin of SapientMLGenerator
Lightning ⚡️ fast forecasting with statistical and econometric models.
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.
Automated modeling and machine learning framework FEDOT
Fast and Accurate ML in 3 Lines of Code
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
Intro to automated machine learning directly from SQL using InterSystems IRIS IntegratedML
Automated Machine Learning on Kubernetes
An open source python library for automated feature engineering
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
A framework for large scale recommendation algorithms.
Public dataset benchmarks used for measuring the performance of MindsDB.
Distributed High-Performance Symbolic Regression in Julia
Allows automatic gradient norm clipping. This feature can help to stabilize training in certain situations by limiting the magnitude of gradient updates. The implementation is inspired by the paper "AutoClip: Adaptive Gradient Clipping for Source Separation Networks"
Successive Halving and Hyperband in the mlr3 ecosystem
A package that makes it trivial to create and evaluate machine learning pipeline architectures.
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