Best AutoML Frameworks for the Developer Community

by July 27, 2020

ML Frameworks

AutoML is still a novice concept, albeit an exciting one with rapid advances in Machine Learning and Deep Learning

What is AutoML? It is perhaps a question that many ML inspired enterprises may ask, to clear the air, Auto Machine Learning or AutoML in short automates all or at least some of the Machine Learning steps without losing predictive accuracy. The ideal AutoML strategy comes from the fact that any user (and that includes citizen data scientists) can take raw data, build a model on it, to get predictions with the best possible accuracy.

The biggest benefit of AutoML technology is the possibility of releasing businesses and data analysts from long routine tasks and instead giving them more time for to strategize and work on the creative side of a project instead.

The data from an Gartner report says that by 2020, 40% of data specialists will be replaced by AutoML. Surprising? This is how fast Machine Learning Technology is changing whcih creates a need for developers to choose the best Auto Machine Learning frameworks which confers to the requirements of their models.

Analytics Insight covers the Best AutoML models for the developer community and the ML savvy enterprises-

 

Auto Sklearn

Auto Sklearn is an automated machine learning toolkit based on Bayesian optimization, meta-learning, ensemble construction. It frees a machine learning user from algorithm selection and hyperparameter tuning.

The Auto Sklearn, AutoML package includes 15 classification algorithms besides 14 for feature pre-processing which defines the right algorithm to optimise parameter accuracy at a precision level of more than 0.98. Auto Sklean translates well for small and medium datasets, however, developers face a hiccup with dealing with large datasets.

 

Tree-Based Pipeline Optimization Tool (TPOT)

It was 2018, that TPOT was put in the list of the most popular auto-machine learning frameworks on GitHub, and the popular AutoML framework has not looked back since then. The TPOT AutoML framework uses genetic programming to zero on a model for task implementation. TPOT AutoML framework can analyse thousands of pipelines to offer one with the best Python code.

TPOT comes with its own regression and classification algorithms. However, its disadvantages include the inability to interact with categorical lines and natural language.

 

ML Box

ML Box is a data Python based library offering the features of read, pre-process, clean and format data with an option to choose specific features and detect a leak.ML Box Auto ML toolkit can classify and regress state-of-the-art models for predictions and model interpreting.

ML Box also offers developers with data preparation, model selection and hyper Parameter Search, however this AutoML toolkit is more suitable for the Linux operating systems. Windows and Mac users can experience some difficulties while installing ML Box.

 

H2O AutoML

H2O AutoML framework is best suited to those who are searching for deep learning mechanisms. H2O AutoML can perform many tasks which requires many lines of code at the simultaneously.

H2O AutoML supports both traditional neural networks and machine learning models. It is especially suitable for developers who want to automate deep learning.

 

Auto Keras

AutoKeras an open-source deep learning framework which is built on network morphism with an aim to boost Bayesian optimization. This AutoML framework can automatically search for hyperparameters and architecture for complex models. AutoKeras conducts searches with the help of Neural Architecture Search (NAS) algorithms to ultimately eliminate the need for deep learning engineers.

This Auto Machine Learning Toolkit follows the design of the classic scikit-learn API, however, it uses a powerful neural network search for model parameters using Keras.

 

Google Cloud Auto ML

Google launched the Google Auto ML framework which integrates the powers of neural network architecture. Its graphical user interface (GUI) is simple to use for model processing models which makes Google Cloud Auto ML useful for citizen developers and citizen data scientists who have limited ML knowledge to process ML-models development.

However, Google Cloud Auto ML is a paid platform, which makes it feasible to use if only for commercial projects. Besides this Auto ML toolkit is available free of charge for research purposes throughout the year.