Broad Learning System is a novel machine learning paradigm offering fast, accurate, and incremental learning without deep structures, suitable for big data environments.
Model Training AI Tools
Discover 14 Model Training tools on AI Tech Suite, including Broad Learning System, KABA.AI and Literal Labs
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KABA.AI is a platform for building and training personalized, private AI models based on your unique actions, experiences, and interests, running locally to ensure data security and ownership.
Deploy logic-based AI models that run 50x faster and use 50x less energy than neural networks on standard CPUs and MCUs without needing expensive GPU hardware.
Train generalized linear models significantly faster using a system-aware library optimized for heterogeneous CPU and GPU clusters in enterprise environments.
Streamline AI research by browsing, training, and comparing PyTorch models through a visual interface that minimizes coding while supporting remote workflows.
Modela is a no-code machine learning platform extending Kubernetes with automatic machine learning capabilities. Train, deploy, and scale ML models with a Kubernetes-native approach.
Accelerate your machine learning projects with expert-led AI research, open-source models, and high-performance GPU computing environments for businesses.
Alpa is a system for training and serving large-scale neural networks.
Store, explore, and train machine learning models directly within an open-source database using SQL and RESTful APIs for rapid real-time deployment.
Train state-of-the-art self-supervised computer vision models with a scalable PyTorch library featuring reproducible SimCLR, MoCo, and SwAV implementations.
Scale deep learning models from days to minutes using a distributed framework that supports PyTorch, TensorFlow, and MXNet with minimal code changes.
Open-source deep learning platform for training models faster, hyperparameter tuning, experiment tracking, and resource management. Supports distributed training and team collaboration.
Achieve state-of-the-art accuracy in machine learning tasks with a scalable gradient boosting library designed for high performance and distributed computing.
Open-source platform for training, evaluating, and deploying LLMs.