Automated machine learning has the ability to increase productivity while decreasing human error. 

Businesses must also be able to address issues on a large scale, for which they are increasingly turning to machine learning. Companies can expedite decision-making with data-driven forecasts by developing algorithms that “learn” over time. However, building the models can be difficult and time-consuming, which can be difficult for organizations with few resources. 

Automated machine learning (AutoML) brings together these two technologies of “Automation” and “Machine Learning” to take advantage of their finest features, enabling businesses to get valuable insights while lowering overall complexity. When AutoML is used, firms may better manage their resources, collect and analyze data, and respond to it promptly. 

Automated Machine Learning (AutoML): What Is It? 

Comparing AutoML to traditional machine learning is a step forward. Numerous steps in the machine learning workflow, such as data preprocessing, feature engineering, model selection, architecture search, and model deployment, can be automated. The format of training data utilized, such as independent, identically distributed (IID) tabular data, raw text, or picture data, can also be used to categorize AutoML deployments. Some AutoML solutions can also handle a variety of data types and techniques.

The benefits of automated machine learning (AutoML) are many, but we’ll start with three: 

  • Automation: AutoML makes it easier for data scientists to develop highly automated machine learning models and do repeated and time-consuming hyperparameter searches over a variety of methods. Just imagine the time savings when you can run a few lines of code and have your model optimized in seconds. 
  • Scalability: As technology develops, it is now possible to develop efficient analogues of particular human learning processes. You can develop models that are scalable, so they can be used on big data sets with ease—and without human intervention! 
  • Ease of use: AutoML is a technology that helps automate the process of applying machine learning to actual issues. The procedures needed to address line-of-business problems are streamlined using AutoML. 

Automated Machine Learning (AutoML): Its Future



Source: Google Trends

A Google trend result showing the volume of “autoML”-related web searches from 2004 to the Present.

AutoML is slowly gaining popularity, and in 2019 the market generated $270 million. By 2030, it is anticipated to have grown at a CAGR of 43.7%, reaching $14,512 million (2020–2030). The statistics demonstrate that interest in AutoML has not reached a peak and will continue to rise. (Research and Market)

AutoML has the incredible potential to completely alter the machine learning landscape, as seen by the growing interest and conversation surrounding it along with early market adoptions. 


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