Within a few years, AutoML has emerged as an exciting new area for both R&D and business applications. It is a process that allows to add many different processes of ML on automation without compromise in accuracy.
AutoML is about generating ML solutions for the data scientist without having to do endless searches on data preparation, data cleansing, model selection, model hyperparameters, ensemble generation parameters, and model compression parameters.
It a quite fast and a dirty way to get great accuracy for your machine learning task without a lot of work. AutoML makes ML more accessible, it reduces human expertise and improves the model’s overall performance.
AutoML majorly focuses on two aspects data acquisition and prediction. It helps in hyperparameter tuning of models, iterative modeling, algorithm selection, model assessment and in many other tasks.
AutoML is not very old technology, the concept of AutoML was first proposed in 2014 at a little-known workshop in the University of Freiburg, Germany. The organizers came with the idea of developing off-the-shelf machine learning methods that would not require any human experts.
Here are the 11 best AutoML tools that can help you to cut out many boring tasks from ML project.
Amazon Lex is the same deep learning technologies that power Amazon Alexa. It is a fully managed service for building conversational interfaces into any application using voice and text
Amazon Lex enables developers to quickly & easily build chatbots with highly engaging user experiences and lifelike conversational interactions, that will ultimately help to engage the customers.
It comes with the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text.
As Amazon Lex is a technology build closely with AWS, Amazon Lex provides built-in integration with AWS Lambda, AWS MobileHub, and Amazon CloudWatch.
Users can also easily integrate with services of AWS like the Amazon Cognito, and Amazon DynamoDB.
AutoFolio is an algorithm selection tool that helps to select a well-performing algorithm for a given instance. As a developer, it saves a lot of time and brain energy that get consume on algorithm selection.
Behind the scene, AutoFolio uses an algorithm configuration to optimize the performance of algorithm selection systems by determining the best selection approach and its hyperparameters.
The algorithm configuration tools AutoFolio uses are called as SMAC. SMAC a well-performing algorithm selection approach and its hyper-parameters for a given algorithm selection data are amazing.
To analyze which choices are best, AutoFolio uses two complementary methods for assessing parameter importance in algorithm configuration spaces, the functional ANOVA for a global measure of parameter importance and ablation analysis for a local measure.
AutoFolio answers two important questions about how to select an algorithm selection approach and how to set its parameters effectively.
AutoFolio algorithm configurators to automatically configure the flexible algorithm framework of clasp folio 2 which implements a large variety of different AS approaches and is highly parameterized.
Therefore, AutoFolio has a robust performance across different algorithm selection tasks.
Scikit-learn is a great tool for ML and with Auto-Scklearn it has become even more amazing. Auto-Scklearn is created by the researchers at the University of Freiburg.
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.
Auto-sklearn provides out-of-the-box supervised machine learning. Preprocessing in auto-sklearn is divided into data preprocessing and feature preprocessing.
Auto-Scklearn does not focus on neural architecture search for deep neural networks but uses Bayesian optimization for hyperparameter tuning for “traditional” machine learning algorithms that are implemented within scikit-learn.
Auto-Keras is also a beautiful tool for AutoML. It is an open-source python package written in the very easy to use deep learning library Keras.
Auto-Keras mainly focuses to reduce the barrier to entry to performing machine learning and deep learning through the use of automated Neural Architecture Search (NAS) algorithms.
Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models. AutoKeras uses ENAS, an efficient and most recent version of Neural Architecture Search.
Auto-Keras utilizes the Neural Architecture Search but applies “network morphism” (keeping network functionality while changing the architecture) along with Bayesian optimization to guide the network morphism for more efficient neural network search.
With Auto-Keras, a programmer with minimal machine learning expertise can apply these algorithms to achieve state-of-the-art performance with very little effort.
TPOT is an open-source Python data science automation tool, which operates by optimizing a series of feature preprocessors and models, in order to maximize cross-validation accuracy on data sets.
TPOT is built on the scikit learn library and follows the scikit learn API closely. It can be used for regression and classification tasks and has special implementations for medical research.
TPOT has what its developers call a genetic search algorithm to find the best parameters and model ensembles. It could also be thought of as a natural selection or evolutionary algorithm.
TPOT tries a pipeline, evaluates its performance, and randomly changes parts of the pipeline in search of better-performing algorithms.
Flexfolio is built on the award-winning, portfolio-based ASP solver claspfolio. It is a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques.
The Flexfolio framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques.
As such, it provides a unique framework for comparing and combining existing portfolio-based algorithm selection approaches and techniques in a single, unified framework.
H2O.ai is an open-source Machine Learning platform that gives developers a good bunch of Machine Learning algorithms to build scalable prediction models.
H2O AutoML helps in many different ways to automate the Machine Learning workflow, which includes training and tuning of hyper-parameters of models.
In H2O the AutoML process can be controlled by specifying a time-limit or defining a performance metric-based stopping criteria. AutoML returns a leaderboard with the best models ensembled.
H2O’s core code is written in Java that enables the whole framework for multi-threading. Although it is written in Java, it provides interfaces for R, Python, and a few others, thus enabling it to be used efficiently.
AutoML provides APIs in Python and R that comes with H2O library.
H2O architecture can be divided into different layers in which the top layer will be different APIs, and the bottom layer will be H2O JVM.
H2O automated platform contains models that provide an initial test ride so that the user doesn’t have to know about the nitty-gritty details of building a model from scratch for autonomous driving.
Pytorch the library managed by Facebook has many important features for Machine Learning and Deep Learning, Auto Pytorch is one of those features that really helps to automate many time-consuming processes in ML.
Finding the right architecture and hyperparameter settings for training a deep neural network is crucial for achieving top performance.
Auto-PyTorch automates these two aspects by using multi-fidelity optimization and Bayesian optimization (BOHB) to search for the best settings.
The current version of Auto-PyTorch is an early alpha and only supports featured data. The upcoming versions will also support image data, natural language processing, speech, and videos.
Many different machine learning algorithms exist that can easily be used off the shelf, many of these methods are implemented in the open-source WEKA package.
However, each of these algorithms has its own hyperparameters that can drastically change their performance, and there are a staggeringly large number of possible alternatives overall.
Auto-WEKA considers the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous methods that address these issues in isolation.
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.
Our hope is that Auto-WEKA will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance.
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.
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.
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