Machine learning uses artificial intelligence (AI) to take patterns from data and learn to make improved decisions in the future.
The purpose of automated machine learning is to makes it easier to build models by automating the manual, time-consuming aspects of machine learning. It incorporates practices from top-ranked data scientists to make the models more accessible for all levels of a business. With automated machine learning, a model that would typically take weeks or months to build can now be completed in days, allowing companies to solve problems faster.
Automated machine learning can clean data and select features, models, and parameters in the background. This allows data scientists to focus on the results and applications of the model versus spending time going through the manual process of building the machine learning model.
Automated machine learning significantly reduces the time and effort it takes to create a machine learning model. Manually completing this process requires computer science and math skills, as well as domain knowledge – this can make it prohibitive for smaller companies or departments to build machine learning models.
Automating these processes also reduces the opportunity for bias and human error, which can degrade the accuracy of a model and the insights it provides. The fact that knowledge from top-ranked data scientists is built into these models allows them to improve the return on investment as well.
There is significant potential for the use of automated machine learning across all industries, from healthcare and manufacturing to financial firms. Since many of the tasks will be automated, business owners will be able to quickly deploy machine learning solutions and allow data scientists to focus on more complex issues.
At LogicPlum, we have invested years of development into our automated machine learning platform to successfully automate all of the steps needed to build, deploy, and maintain machine learning models for your business.
Our experts can help you easily understand the features that were used and the algorithms that were generated, as well as determine what will have the most significant impact on your results. Our model will provide human-readable explanations for predictions – by doing so, we can build trust in automated machine learning and help businesses can make critical decisions with ease.