There are many AutoML tools available on the market today, each with its own set of features and capabilities. Here are a few AutoML tools that make machine learning pipeline building relatively effortless:
Auto-Keras :
An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. It is an open-source software library for automated machine learning (AutoML). Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models.
Google AutoML :
Google's AutoML suite includes several different tools for building and training machine learning models, including AutoML Vision, AutoML Natural Language, and AutoML Translation. These tools use Google's advanced machine learning algorithms to automate the process of model selection, hyperparameter tuning, and deployment.
H2O.ai :
H2O.ai is an open-source AutoML platform that provides a suite of tools for building and training machine learning models. H2O.ai includes several different algorithms for classification, regression, and clustering, as well as tools for feature engineering and model interpretation.
DataRobot :
DataRobot is a commercial AutoML platform that provides a comprehensive suite of tools for building and deploying machine learning models. DataRobot includes several different algorithms for classification, regression, and time-series forecasting, as well as tools for automated feature engineering and model interpretation.
TPOT :
TPOT is an open-source AutoML tool that uses genetic algorithms to automate the process of model selection and hyperparameter tuning. TPOT includes several different algorithms for classification, regression, and clustering, and can generate optimized pipelines for data preprocessing, feature engineering, and model evaluation.
Microsoft Azure AutoML :
Microsoft's Azure AutoML provides a suite of tools for building and deploying machine learning models on the Azure cloud platform. Azure AutoML includes several different algorithms for classification, regression, and time-series forecasting, as well as tools for automated feature engineering and model interpretation.
Amazon LEX :
Amazon Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) and enables the user to build applications with highly engaging user experiences and lifelike conversational interactions.
Amazon Lex makes Amazon Alexa available to all developers allowing them to quickly and easily build sophisticated, natural language, conversational bots.
Auto-WEKA :
Auto-WEKA considers the problem of simultaneously selecting a learning algorithm and setting its hyperparameters. Auto-WEKA does this using a fully automated approach, leveraging recent innovations in Bayesian optimization and helping non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications.
RoBO :
a Robust Bayesian Optimization framework is written in Python. The core of RoBO is a modular framework that allows for the easy addition and exchange of components of Bayesian optimization such as different acquisition functions or regression models.
It contains a variety of different regression models such as Gaussian processes, Random Forests, or Bayesian neural networks, and different acquisition functions such as expected improvement, probability of improvement, lower confidence bound, or information gain.
AutoFolio :
AutoFolio uses algorithm configuration to optimize the performance of algorithm selection systems by determining the best selection approach and its hyperparameters.
Algorithm selection (AS) techniques which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently have substantially improved the state-of-the-art in solving many prominent AI problems.
Overall, there are many AutoML tools available on the market today, each with its own strengths and weaknesses. When selecting an AutoML tool, it is important to consider factors such as ease of use, flexibility, scalability, and cost, as well as the specific requirements of your application or use case.