#AutoML

AutoGluon automates machine learning tasks to deliver high predictive accuracy with minimal coding effort. It supports diverse data types, including images, text, time series, and tabular data, and is compatible with Python versions 3.8 to 3.11 on Linux, MacOS, and Windows. The tool offers comprehensive documentation, tutorials, and a supportive community through platforms such as Discord and Twitter, making it an effective choice for developers looking to improve machine learning processes efficiently.

AutoKeras, originating from Texas A&M University's DATA Lab, offers a streamlined approach to deep learning with AutoML features. Designed for both beginners and professionals, it provides a user-friendly platform to develop machine learning models with ease. Supporting Python 3.8+ and TensorFlow 2.8+, AutoKeras comes with tutorials and projects to aid learning. Installation through pip enables the application of advanced tools, including image classification. As a community-supported initiative, contributions are encouraged on GitHub. Discover how AutoKeras makes advanced machine learning accessible to all.

Gorse is an open-source recommendation engine that effortlessly integrates into various online services. With features like multi-source recommendations and AutoML for model optimization, it supports both scalability and flexibility. Users benefit from RESTful APIs, online evaluation, and a straightforward dashboard for efficient management. Ideal for both beginners and advanced users, Gorse offers quick setup through playground mode and adaptable architecture, making it a powerful tool for modern web services.

TransmogrifAI is an AutoML library designed for efficient machine learning development on Apache Spark. It emphasizes productivity through features such as compile-time type safety and modular design, allowing for rapid development of accurate machine learning models. Ideal for those seeking to build production-ready models quickly without the need for in-depth machine learning expertise, it offers flexibility in feature specification and model selection. Access comprehensive documentation and examples to maximize its potential.

Explore a curated selection of MLOps tools designed to enhance automation and optimization in machine learning workflows. Categories include AutoML, data management, CI/CD, and lifecycle management. Each tool is chosen for its ability to increase efficiency, support model fairness, and improve deployment outcomes. This collection is indispensable for data scientists and engineers aiming to streamline their MLOps workflows and effectively deploy high-performance ML models.

Explore diverse ML.NET samples created to assist .NET developers in integrating machine learning into their projects, featuring examples such as binary classification, regression, and anomaly detection. The repository includes practical console and end-to-end app examples, while emphasizing community contributions and offering CLI and AutoML API tools for automated model creation. Discover related tutorials and guides to deepen understanding, all presented in an objective and neutral manner.

FLAML is a lightweight Python library that excels at AutoML and hyperparameter tuning across various tasks, such as classification and regression. With minimal computational requirements, it offers extensive customization for optimizing machine learning models and next-gen GPT-X applications using automated multi-agent frameworks. The library is perfectly designed to handle complex constraints while integrating seamlessly with MLflow and Microsoft Fabric Data Science for comprehensive MLOps/LMOps solutions.

Discover a wide array of tutorials and training materials for H2O-3 designed to improve comprehension and utilization of its features. Delve into guides covering aspects like H2O Grid Search, Model Selection, Deep Learning, Stacked Ensembles, and AutoML available in both R and Python. Access up-to-date materials to keep pace with H2O's developments. Benefit from resources for self-improvement or event preparation like H2O World. Engage with the H2O community through Stack Overflow and the H2O Stream Google Group for support and shared insights.

Explore a curated list of the latest Automated Machine Learning (AutoML) research papers, articles, and projects. Gain insights into vital AutoML processes such as data preprocessing, feature selection, hyperparameter optimization, and performance prediction. Reflecting on the growing corporate interest, it illustrates how major companies and startups are developing AutoML systems, showcasing their potential to rival human expertise. An essential resource for those seeking to delve into or contribute to the evolving AutoML landscape.

Explore Kubernetes-native AutoML that seamlessly automates processes like hyperparameter tuning, early stopping, and neural architecture search across multiple frameworks such as TensorFlow, PyTorch, and MXNet. This open-source project integrates efficiently with Kubernetes resources and tools such as Kubeflow Training Operator and Argo Workflows, supporting algorithms including Random Search and Bayesian Optimization. Discover framework compatibility with Goptuna, Hyperopt, and Optuna, and initiate efficient model tuning with the Python SDK.

Lightwood simplifies the machine learning process by allowing customization with JSON syntax. It supports various data types for complex tasks like time-series analysis, enabling users to tailor machine learning pipelines without writing repetitive code.

This repository offers a PyTorch implementation of techniques detailed in the paper 'AMC: AutoML for Model Compression and Acceleration on Mobile Devices'. It includes a methodological workflow for compressing MobileNet models on ImageNet, covering strategy search, weight export, and fine-tuning. The code enables replication of the compression process, facilitating significant FLOPs reduction without compromising accuracy. Pre-compressed MobileNet models are accessible in PyTorch and TensorFlow formats, alongside detailed performance statistics.