Automated Machine Learning (AutoML) is a booming subfield of Machine Learning. It is a blessing for the non-technical users willing to get their feet wet and hands dirty. Many portray the AutoML tools as something that is eventually going to take the place of the data scientists; that they are going to somehow revolutionize the way today’s data analysis pipeline works.
But the reality is that they are primarily aimed towards statistically less fluent users. They can be helpful for the newcomers who would prefer seeing the models at work rather than getting stuck in the mundane stuff. Another use case of the AutoML tools is in making the data scientists and the ML engineers more productive by simplifying the process of coming up with an appropriate model.
No matter how lucrative AutoML sounds, in theory, there is always some catch to it. One of the reasons why many experts despise the idea of using AutoML tools is that they can many times introduce bias into the model. Most of the complex models are very hard to interpret. This makes the task of identifying when the bias is being introduced or what is causing it to creep into the model even harder.
As most of the pre and post-processing tasks performed automatically, AutoML introduces a layer of abstraction. That being said, these tools offer a plethora of opportunities for newbies or less proficient personnel.
The importance of the Automated Machine Learning
Constructing a model based on numerical data is a long process consisting of multiple steps – often requiring the involvement of multiple domain experts. It requires domain knowledge as well as a great deal of mathematical expertise. Often this whole process becomes practically intractable for a small-scale organization.
Even if your organization is able to afford a group of statisticians and data scientists, many other factors can degrade the process. Human errors and bias is one reason behind the underperformance of a model.
Whatever may the reason be, automated machine learning provides feasible solutions to the earlier problems. These tools have widespread applications irrespective of the industry – be it fintech, retail, or healthcare. AutoML is the way to liberalizing AI and ML of the grasp of the giants. By automating many aspects of the whole pipeline, these tools make it possible to deploy AI-powered applications and solutions with ease. This leaves the experts to work on the more complex parts that need their attention.
1. Amazon Lex
Amazon Lex provides a host of Natural Language Processing features for automating the Machine Learning pipeline. It provides deep learning features like automatic speech recognition and natural language understanding. This gives users the necessary tools to build applications with engaging user experience and lifelike interaction. It integrates with Amazon Alexa seamlessly allowing the devs to build sophisticated bots with natural language understanding.
Originally developed at Texas A&M University, AutoKeras is an open-source library based on Keras for automated machine learning. It was originally developed by Haifeng Jin, Qingquan Song, and Xia Hu. Many other contributors contributed significantly along the way to make it one of the most popular tools. The goal of this tool is to make machine learning accessible to everyone by abstracting out most of the mathematical intricacies. It provides functionalities for automatically searching for the architecture and hyperparameters of the models.
Auto-WEKA is an automated machine learning tool built for automatically finding the best model with its best parameter settings for a given task – be it classification or regression. In other words, it simultaneously selects an algorithm and the corresponding hyperparameters. It can achieve this in a fully automated way using some of the recent findings like Bayesian optimization. It can help even the non-expert, non-technical users utilize machine learning for their applications.
4. H2O AutoML
H2O is a distributed platform designed for simplifying the whole data analysis workflow. AutoML is an open-source, distributed, and in-memory automated machine learning toolset that is a part of the H2O platform. It is used for automating the ML pipeline, which often includes data cleaning, feature selection, model selection, and parameter selection. It can be used for automatic training, tuning the models, and automatic parameter selection in a user-specified time limit.