What is Automated Machine Learning? (AutoML)



Even though AutoML is not a very uncommon name these days, there is no universally agreed definition to it. Basically, Automated Machine Learning is focused on automating repetitive tasks of the machine learning process. Definitely, machine learning is an automated process in itself, but the preparation for the machine learning process requires a data expert to work with it.

If you are a data scientist or you have data scientists in your team, you probably will agree that data pre-processing, selecting explanatory variables, doing feature engineering, choosing algorithms, tuning hyperparameters and algorithm application are time-consuming and tedious tasks.

When to use Automated Machine Learning?

AutoML gets handy when:

  • A firm doesn’t have enough data experts and needs to improve their productivity and decrease their endless backlog of modeling projects;
  • There is no access to data experts at all, but there is a need for predictive modeling solutions to solve business problems.

The ability to build and run models fast is not the only advantage of automated machine learning solutions. Among the other benefits, AutoML solutions eliminate human error, encourage high accuracy level, and last but not least it offers a user-friendly interface accessible to non-machine learning experts.

Today, different automated machine learning solutions target various stages of the machine learning process. A very common approach is to parallelize concurrent execution of hundreds of machine learning algorithms and rank the best ones, but they still require specific data preparation and high CPU/GPU costs.

Automated Machine Learning examples of application 

  • Scientific research and disease prediction. The early prediction of cancer, for example, has become a necessity. Despite access to quality data, researchers often cannot identify key biomarkers and build predictive models without data scientists. Automated machine learning solutions provide them with this opportunity.
  • Supply chain management. Automated machine learning can help identify products that are at risk of backorder in advance, giving supply chain managers time to react before the event occurs.
  • Predictive analytics in agriculture is used to analyze historical data and current data to improve agronomic opportunities. Automated machine learning helps producers make fast important decisions out of sensor information.
  • Industrial process optimization. Automated Machine Learning helps digitally recreate how industrial businesses operate in order to increase their productivity.
  • Predictive maintenance has become a must-have solution in manufacturing. Automated machine learning is used to avoid unplanned downtime and predict the next failure of a part, machine or system.
  • Production quality improvement. Automated machine learning is used to digitally reinforce quality control, including raw material selection.

Other examples:

  • Failure prediction
  • Anomaly detection
  • Fraud prediction
  • Industrial asset optimization
  • Real estate prices prediction

MyDataModels and Automated Machine Learning

Our vision at MyDataModels is to give machine learning access to every professional in every domain and this is why we have created TADA. TADA is the automated machine learning solution that does not require coding or machine learning skills and can build and run models in a few clicks from Small Data. To appreciate all the benefits of our AutoML solution, start using TADA now, it’s free.