All About Automated Machine Learning (AutoML)
Posted: Sep 23, 2022
Automated Machine Learning (AutoML) is one of the most exciting sub-fields of Data Science right now. It sounds fantastic for those unfamiliar with machine learning but terrifying for current Data Scientists. The media portrayal of AutoML makes it appear capable of completely revolutionizing the way we create models by eliminating the need for Data Science. While some companies, such as Data Robot, aim to automate the machine learning process fully, most of those in the field are developing AutoML as a tool to increase the productivity of current Data Scientists and simplify the process for those entering the field to make it more accessible.
Using AutoML to automate the process fully is a fantastic idea, but it introduces numerous opportunities for bias and misunderstanding in practice. In recent years, machine learning has shifted away from "black-box" models and toward simpler, easier-to-interpret models. Complex models can be challenging to decipher, making it difficult to determine when a model introduces bias. AutoML now exacerbates the black box model problem by hiding not only the model's mathematics but also performing the following in the background:
Goals of Automated Machine Learning
AutoML aims to automate data scientists' routine tasks such as feature selection, feature extraction, meta-learning, and transfer learning, among others.
However, it would give Data Scientists more time to diagnose modem issues, improve model performance, and assist models in adapting to possible changes in conditions and data.
We should clarify a few points. The data sourcing process cannot be automated, and only a tiny portion of the data cleansing process can be automated, but the modeling process is becoming automated. Model democratization is a media term. That is, simplify the modeling process so that anyone with basic technical skills can do it.
Under the hood, Google AutoML Tables employs gradient boosters and neural nets. The end result is incredible. It will outperform 99% of all competitors in the space, and I mean those who know what they're doing.
What's the big deal about AutoML Tables?
Because structured data is used in 99% of machine learning applications, that means that the majority of real-world problems are classified and regressed. Gradient boosters are thus the undisputed kings of this domain. With AutoML Tables, you can now model better than 99% of the world's experts.
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Why is Automated Machine Learning Important?
Automated Machine Learning is a powerful field of study in Data Science. For those who are unfamiliar with machine learning and experienced data scientists. The way AutoML has been presented in the media makes it appear capable of completely revolutionizing the way we build models by eliminating the need for data scientists.
Despite this, we are working on developing AutoML as a tool to increase the productivity of active data scientists and simplify the method to make it more accessible to those new to the industry. That problem has now been solved thanks to the introduction of MLops platforms and applications that assist machine learning life-cycle management in automating ML training.
AutoML enables data scientists to increase their efficiency and realize their full potential by automating machine learning tasks such as pipeline development and hyperparameter tuning. In this article, we looked at some of the most common AutoML frameworks and tools.
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