As Automated Machine Learning (AutoML) grows in popularity, there is still no real agreement on a definition of it. Essentially, Automated Machine Learning focuses on automating repetitive tasks of the Machine Learning process. Of course, Machine Learning always employs some form of an automated process, but across different algorithms and tools, there are huge differences in how much of the Machine Learning workflow is actually automated.
Indeed, data preparation tasks such as data pre-processing, explanatory variables selection, feature engineering, algorithm choice, hyperparameter tuning, and algorithm application are time-consuming, tedious, and sometimes more of an art than a science. So, is it possible to automate these tasks? That’s what Automated Machine Learning does.
When to use Automated Machine Learning?
Companies can use AutoML when:
- Data experts need to improve their productivity and decrease their backlog of modeling projects;
There are significant needs for predictive modeling solutions to solve business problems, but not enough data expertise is available.
- AutoML solutions can reduce the impact of human error, improve accuracy, and offer user-friendly interfaces and workflows to non-data experts.
AutoML solutions target numerous stages of the Machine Learning process. A common approach is to parallelize the execution of hundreds of Machine Learning algorithms and rank them, but they still require specific data preparation, high compute costs and often still require some degree of data science knowledge to assess and select models.
Automated Machine Learning examples of application
- Medical Research: 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.
- Predictive maintenance is a must-have solution in manufacturing. As sensors continuously produce data, production experts can use Automated Machine Learning to avoid unplanned downtime and predict the next failure of a part, machine or system.
- Marketing efficiency: marketing experts have access to quality data related to their previous marketing campaigns. To better predict the outcome of their future campaigns and adapt their messages, they can use AutoML solutions.
MyDataModels and Automated Machine Learning
At MyDataModels, our vision is to give access to Machine Learning to every professional in every domain. This is why we created TADA, an Automated Machine Learning solution to build predictive models out of their Small Data. TADA can be used with no prior knowledge of code or data science. To fully enjoy the benefits of TADA, our AutoML solution, you can create your free account and build your first predictive models now!