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10 Popular AutoML Tools Developers Can Use 


10 Popular AutoML Tools Developers Can Use 


In a bid to promote democratisation and to fill in the gaps in domain expertise, AutoML or automated machine learning came to the fore.



There is a lot of talk about democratisation of data science nowadays. But, who is doing what? Datasets are being open-sourced by tech majors along with frameworks and platforms that can assist the user in deploying pretrained models.  

Here are few AutoML tools that make machine learning pipeline building relatively effortless:

Auto-Keras

Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab at Texas A&M University and community contributors. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models.

H20AutoML

H2O is an open source, distributed in-memory machine learning platform with linear scalability. H2O includes an automatic machine learning module also called H2OAutoML which can be used for automating the machine learning workflow,  which includes automatic training and tuning of many models within a user-specified time-limit.

Whereas, H2O.ai’s flagship product Driverless AI is for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment.

SMAC

SMAC (sequential model-based algorithm configuration) is a versatile tool for optimizing algorithm parameters. SMAC is very effective for hyperparameter optimization of machine learning algorithms, scaling better to high dimensions and discrete input dimensions than other algorithms.

AUTO-SKLEARN

Auto-sklearn provides out-of-the-box supervised machine learning. Built around the scikit-learn machine learning library, auto-sklearn automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its hyperparameters. 

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 the developers allowing them to quickly and easily build sophisticated, natural language, conversational bots.

AUTOWEKA

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 help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications.

AUTO-PYTORCH

Auto-PyTorch automates  right architecture and hyperparameter settings by using multi-fidelity optimization and Bayesian optimization (BOHB) to search for the best settings.

See Also

ROBO

RoBO – a Robust Bayesian Optimization framework written in python. The core of RoBO is a modular framework that allows to easily add and exchange 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 function 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.

FLEXFOLIO

Flexfolio, a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques. It provides a unique framework for comparing and combining existing portfolio-based algorithm selection approaches and techniques in a single, unified framework.

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