What is AutoML?

AutoML is the process of automating the process of applying Machine Learning to real-world problems. AutoML covers the full pipeline from the raw dataset to the deployable ML model and was proposed as an AI-based solution to the ever-growing challenge of applying machine learning.

AutoML is now able to perform data pre-processing as well as Extraction, Transformation, and Loading Tasks (ELT). Hence, AutoML allows highly skilled data scientists to reduce the skills gap and build models that use the best diagnostic and predictive analytics tools.

Some AutoML packages can do model selections, scoring, and hyperparameter optimization automatically while other services can help determine the algorithm that is the best for the data.

The Impact of AutoML

ML is slowing taking center stage in the growth of many industries across retail, banking, and healthcare, to mention a few, and, in the long term, will only become more crucial in their development. More and more data science tasks will be automated in the future and thus, there is an evident need for data science talent and skills to achieve this.

This is where AutoML comes to play. Indeed, AutoML can help non-tech companies with less data science expertise in building their own ML applications. If more tasks are automated, data scientists will be able to save both time and cost. AutoML can thus unburden these organizations and make AI more accessible to everyone by automating complex manual data science processes.

For instance, the new Cloud AutoML by Google gives AI/ML experts the possibility to be more productive and help build a powerful AI system for their company.

The Benefits of AutoML

The new generation of AutoML platforms is rapidly evolving to provide AI-focused data preparation, feature engineering automation, ML automation, and automated production. By automating the entire data science process, AutoML is providing faster delivery of insights going from months to days. Hence, ML processes take less time to implement with AutoML, and with more firms now investing in big data and AI, AutoML is becoming a vital part for organizations to process large data in the future.

AutoML technologies can build production-ready models quickly, without having to spend expensive data science, and can provide companies the abilities to use data-driven application which are powered by statistical models. The combination of ML, AI, and deep learning will, in time, bridge the skills gap in the data science industry.

The future is AI-driven

Having full-cycle data science automation enables organizations to avoid investing in many skilled data scientists and teams of engineers and empower citizen data scientists to make AI more accessible. With AutoML, businesses are finally able to stay on top of their projects all the while remaining accountable for their data-driven decisions and meeting the compliance requirements.

Therefore, AutoML provides an essential solution for companies to have the necessary skills for more AI-driven processes. In the long-term, having newly skilled AI/ML developers will be a game-changer to increase the productivity and efficiency of organizations.

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