Cloud AutoML API client for Node.js
This packages provides a set of APIs to load and run models produced by AutoML Edge.
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AutoML engine for wlearn: search space sampling, random search, successive halving, ensemble construction
Build AutoML pipelines
OpenClaw long-term memory plugin backed by SynapCores AIDB (LanceDB-parity API + SQL filtering, graph relations, AutoML relevance)
TMLC AutoML SDK for JavaScript
drift — terminal-first, chat-based AutoML. Open source. No tokens. No auth.
nlp-automl 2019-11-11 Node.js SDK
Convenience barrel: re-exports all wlearn model classes, automl, ensemble, pipeline, and core utilities
An open-source machine learning framework.
Comprehensive Machine Learning plugin with 10 specialist agents: TensorFlow/Keras, PyTorch, RL, Scikit-learn, Neural Architecture, Gradient Boosting, Computer Vision, NLP Transformers, Time Series, and AutoML. Context7-verified patterns.
HiTechClaw ML/AutoML Engine — Dataset management, model training, AutoML pipelines, and model registry
DataFire integration for Cloud AutoML API
70+ ML algorithms across 15 families with AutoML, SIMD acceleration, and zero dependencies. Rust/WASM, browser + Node.
#### 安装
xClaw ML/AutoML Engine — Dataset management, model training, AutoML pipelines, and model registry
This package adds a WebAssembly backend to TensorFlow.js. It currently supports the following models from our [models](https://github.com/tensorflow/tfjs-models) repo: - BlazeFace - BodyPix - CocoSSD - Face landmarks detection - HandPose - KNN classifier
This package adds a GPU accelerated [WebGPU](https://www.w3.org/TR/webgpu/) backend to TensorFlow.js. It currently supports the following models: - BlazeFace - BodyPix - Face landmarks detection - HandPose - MobileNet - PoseDetection - Universal sentence
Vertex AI client for Node.js
An npm package to make it easier to use vertex AI with Google autoML for image classification
LightAutoML by SberAILab Machine learning package for node-red.
hyper-parameter validator for automl project
* 基于 typescript 的常用函数库。 * Flowengine组件开发SDK。
Automated machine learning for classification and regression
Streaming ML in Rust -- gradient boosted trees, neural architectures (TTT/KAN/MoE/Mamba/SNN), AutoML, kernel methods, and composable pipelines
A Google APIs client library generated by tonic-build
A Google APIs client library generated by tonic-build
Tantale is a Rust library for Automated Machine Learning (AutoML) focusing on Hyperparameter Optimization and Neural Architecture Search. It provides a modular and extensible framework for defining search spaces, objectives, and optimization algorithms, with support for distributed computing and parallel execution.
Tantale is a Rust library for Automated Machine Learning (AutoML) focusing on Hyperparameter Optimization and Neural Architecture Search. It provides a modular and extensible framework for defining search spaces, objectives, and optimization algorithms, with support for distributed computing and parallel execution.
Tantale is a Rust library for Automated Machine Learning (AutoML) focusing on Hyperparameter Optimization and Neural Architecture Search. It provides a modular and extensible framework for defining search spaces, objectives, and optimization algorithms, with support for distributed computing and parallel execution.
Tantale is a Rust library for Automated Machine Learning (AutoML) focusing on Hyperparameter Optimization and Neural Architecture Search. It provides a modular and extensible framework for defining search spaces, objectives, and optimization algorithms, with support for distributed computing and parallel execution.
Tantale is a Rust library for Automated Machine Learning (AutoML) focusing on Hyperparameter Optimization and Neural Architecture Search. It provides a modular and extensible framework for defining search spaces, objectives, and optimization algorithms, with support for distributed computing and parallel execution.
Tantale is a Rust library for Automated Machine Learning (AutoML) focusing on Hyperparameter Optimization and Neural Architecture Search. It provides a modular and extensible framework for defining search spaces, objectives, and optimization algorithms, with support for distributed computing and parallel execution.
Tantale is a Rust library for Automated Machine Learning (AutoML) focusing on Hyperparameter Optimization and Neural Architecture Search. It provides a modular and extensible framework for defining search spaces, objectives, and optimization algorithms, with support for distributed computing and parallel execution.
A native Rust AutoML pipeline toolkit
AutoML makes the power of machine learning available to you even if you have limited knowledge of machine learning. You can use AutoML to build on Google's machine learning capabilities to create your own custom machine learning models that are tailored to your business needs, and then integrate those models into your applications and web sites.
AutoML makes the power of machine learning available to you even if you have limited knowledge of machine learning. You can use AutoML to build on Google's machine learning capabilities to create your own custom machine learning models that are tailored to your business needs, and then integrate those models into your applications and web sites. Note that google-cloud-automl-v1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-automl instead. See the readme for more details.
AutoML makes the power of machine learning available to you even if you have limited knowledge of machine learning. You can use AutoML to build on Google's machine learning capabilities to create your own custom machine learning models that are tailored to your business needs, and then integrate those models into your applications and web sites. Note that google-cloud-automl-v1beta1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-automl instead. See the readme for more details.
Document AI uses machine learning on a single cloud-based platform to automatically classify, extract, and enrich data within your documents to unlock insights. Note that google-cloud-document_ai-v1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-document_ai instead. See the readme for more details.
Document AI uses machine learning on a single cloud-based platform to automatically classify, extract, and enrich data within your documents to unlock insights. Note that google-cloud-document_ai-v1beta3 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-document_ai instead. See the readme for more details.
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