What Is Automated Machine Learning?

Automated Machine Learning (abbreviated as “AutoML”) is an innovative approach that allows software to construct and refine its own machine learning process, freeing up human employees to conduct more complex operations. What does that mean for your business? Automated Machine Learning can make your data analysis process faster and allow it to generate more accurate results, all while minimizing the need for you to employ data scientists on staff to handle these operations.???

Automated Machine Learning Basics

For context, AutoML is part of the larger field of artificial intelligence, a capability of certain machines to perform tasks that normally require human intelligence, using models composed of algorithms and data. Machine learning describes the process by which artificial intelligence analyzes results and assimilates information in order to improve its own efficiency, traditionally with some human assistance to refine it.?

AutoML automates machine learning so that human input is not required to facilitate it. In practice, this means that human employees do not have to input data and make adjustments in order to improve the model; with AutoML the artificial intelligence-enabled software will do all of this on its own, refining its own model to collect and analyze data more accurately.?

To be more specific, AutoML allows software to independently pre-process data, engineer, extract, and select features, select algorithms, and optimize hyperparameters.?

How Can Automated Machine Learning Help Your Business?

In the business context, AutoML brings the benefits of increased speed of operations and accuracy of data, as well as making the utility of machine learning accessible for employees who may not have advanced competency in data science.?

The speed benefits of automating a manual process may be the most intuitive benefits of AutoML, as computers generally complete tasks faster than humans do. More specifically, since your employees will not have to manually update the machine learning model based on the results it generates, the speed of this process will increase.?

Secondly, you could receive more accurate data when your analysis is powered by AutoML. By removing humans from the model-tuning process, the potential for human error decreases greatly.?

Finally, because AutoML removes the necessity for highly trained data scientists to be involved at every stage of the machine learning process, it increases the accessibility of machine learning for organizations that do not employ such scientists. After assistance with setup and minimal training, employees without a data science background can use and maintain these systems.?

AutoML Doesn’t Mean Autopilot?

Keep in mind that one of the primary goals of AutoML is to assist (not replace) data scientists with model training and evaluation.? Outside of that data scientists still have their hands full with a host of other tasks (i.e, data preparation, feature engineering, model deployments). Having help with model training and evaluation does improve efficiency, but data scientists need to keep paying close attention to how AutoML puts models in play.? Data scientists are essential, especially considering that sometimes AutoML models can be somewhat constricted. This makes sense because those models are packaged to perform a certain way, and tuning requires the careful eye of a data scientist to ensure maximum output.

Another thing to consider is that AutoML still struggles with unsupervised machine learning and reinforcement learning.? To be fair, the capability continues to evolve, and I believe that overtime, AutoML will be able to assist data scientists with almost any type of machine learning challenge.?

How to Get Started with AutoML

The best way to implement AutoML in your business is to partner with machine learning experts to ensure that your initial setup is tailored to your business’ needs. Defining your business objectives upfront are key to leveraging AutoML. You need to ask yourself what metrics are we looking to predict.? Square Peg Technologies has extensive experience applying machine learning and predictive analysis in a wide variety of organizations. Vendors that we have worked with in the past are DataRobot, a leading AutoML platform, and Google Cloud AutoML.? Both have comparable offerings but have different value propositions, especially considering that Google AutoML is a native part of their cloud offering.? Both have proven to help employees who have minimal machine learning knowledge train and optimize models, a critical phase of a successful AutoML implementation.?

Contact Square Peg today to learn how to get started with AutoML for your business.?