AutoML: Automated machine learning

As we may know, how popular nowadays machine Learning technologies are. It is being currently utilized in almost every field imaginable filed which has pushed its importance infinitely. But what about those peoples who don’t have any idea about Machine Learning? That’s where the need of Automated machine learning or AutoML comes into picture.

Automated machine learning , basically when we try automating the end-to-end process of applying any machine learning to real-world problems that are actually relevant in the industry.

Automated machine learning (AutoML) is basically when we try automating the end-to-end process of applying any machine learning to real-world problems that are actually relevant in the industry. In recent years, it has been noticed as well as proven time and time again that ML or machine learning is the key to the future for many businesses. It is understandable that this is an up and coming technology that allows for various directions of research, analysis, and implementation.

However, the use of this vast and powerful technology is limited to the number of data scientists and machine learning enthusiasts and researchers, which are low in the number and slowly rising. To bridge this gap the theory or concept of Automated Machine Learning came into the picture. In any machine learning project, a data the scientist has to apply many different techniques such as the data collection, data pre-processing, feature engineering, feature extraction, and feature-selection methods that make the dataset ready for inference and hence for data analysis. Following those pre-processing steps, an algorithm must be appropriately selected and hyper-parameter optimization must be performed to maximize the predictive performance of their final machine learning model.

As many of these steps can only be performed by ML experts, So, seeing the popularity of machine learning AutoML was proposed for the peoples who don’t have much knowledge about is as an artificial intelligence-based solution to the challenge of easily applying machine learning without much expertise.

Some of the popular AutoML tools available are list below:

It is important that this field of Automated machine learning is researched on and more communities are included as it is an area of utmost importance and a field of untapped potential. Some of them are mentioned below-

Google one of the leading tech-giants has released the Cloud AutoML for making custom machine learning models based on business to business.

Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology.

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. The goal of AutoKeras is to make machine learning accessible for everyone.

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.

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. 

There are some others AutoML tools are available in the market which is being used for specific purposes.

Thus, we can conclude, AutoML maybe a new field as of now, however, it has boundless opportunities and may even be a completely new field of machine learning in the future where a person with no data science or machine, learning background can use these tools very comfortably to find some hidden insights may apply to their business model to be ahead of their competitors.

I hope after reading this article, finally, you came to know aboutAutoML and some of the different and popular AutoML tools available in the market. For more details about different tools I have attached their official website links and I recommend you to visit once.

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