Top 10 AutoML Examples
Automated Machine Learning (AutoML) techniques have been developed to automate the process of designing and training deep learning models. In today’s data-driven world, AutoML has emerged as a game-changer, revolutionizing the way industries harness the potential of machine learning. With its automated approach to building and optimizing models, AutoML empowers businesses across sectors to leverage the power of AI without the need for extensive data science expertise.
The “top” AutoML tools may vary depending on various factors such as specific requirements, use cases, and the evolving landscape of AutoML. However, here are some well-known and widely used AutoML tools that have gained popularity:
H2O.ai offers a suite of AutoML tools, including H2O AutoML, which automates the machine learning model selection and hyperparameter optimization process.
Google Cloud AutoML provides a range of AutoML tools, such as AutoML Vision, AutoML Natural Language, and AutoML Tables, which automate the development of computer vision, NLP, and tabular data models.
Microsoft Azure AutoML simplifies the process of building and deploying machine learning models with automated model selection, hyperparameter tuning, and feature engineering.
4. DataRobot
DataRobot is an enterprise-grade AutoML platform that automates the machine learning workflow, from data preparation and feature engineering to model training and deployment.
IBM Watson AutoAI offers an AutoML solution that automates the creation and deployment of machine learning models, allowing users to focus on business insights rather than technical complexities.
Databricks AutoML provides an automated machine learning platform that enables users to build, tune, and deploy machine learning models at scale on their Databricks environment.
7. Auto-Keras
AutoKeras is an open-source AutoML library built on top of Keras, a popular deep learning framework. It simplifies the process of building and deploying machine learning models by automating key tasks such as model architecture search, hyperparameter tuning, and neural architecture search.
8. TPOT
TPOT (Tree-based Pipeline Optimization Tool) is an open-source AutoML library that employs genetic programming to automate the creation and optimization of machine learning pipelines.
9. MLBox
MLBox is an open-source AutoML library that simplifies the process of building machine learning models. It provides automated solutions for data preprocessing, feature selection, model selection, and hyperparameter tuning. MLBox is designed to handle both structured and tabular data, making it suitable for a wide range of machine learning tasks.
Hugging Face AutoNLP is an AutoML tool specifically designed for natural language processing (NLP) tasks, automating the process of training and fine-tuning NLP models.
