Automated Machine Learning
What is Automated Machine Learning?
Automated machine learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Applying traditional machine learning methods to real-world business problems is time-consuming, resource-intensive, and challenging. It requires experts in the several disciplines, including data scientists – some of the most sought-after professionals in the job market right now.
Automated machine learning changes that, making it easier to build and use machine learning models in the real world by running systematic processes on raw data and selecting models that pull the most relevant information from the data – what is often referred to as “the signal in the noise.” Automated machine learning incorporates machine learning best practices from top-ranked data scientists to make data science more accessible across the organization.
Here is the standard machine learning process at a high level:
When developing a model with the traditional process, as you can see from Figure 1, the only automatic task is model training. Automated machine learning software automatically executes all the steps outlined in red – manual, tedious modeling tasks that used to require skilled data scientists. The traditional process often takes weeks or months, but with automated machine learning, it takes days at most for business professionals and data scientists to develop and compare dozens of models, find insights and predictions, and solve more business problems much faster.
Figure 2 shows the automated machine learning process after uploading a dataset and choosing the target variable for the business problem:
Automating these steps allows for greater agility in problem-solving and the democratization of data science to include those without extensive programming knowledge.
Why is Automated Machine Learning important?
Manually constructing a machine learning model is a multistep process that requires domain knowledge, mathematical expertise, and computer science skills – which is a lot to ask of one company, let alone one data scientist (provided you can hire and retain one). Not only that, there are countless opportunities for human error and bias, which gets in the way of model accuracy and devalues the insights you might get from the model. Automated machine learning enables organizations to use the baked-in knowledge of data scientists without having to develop the capabilities themselves, simultaneously improving return on investment in data science initiatives and reducing the amount of time it takes to capture value.
Automated machine learning makes it possible for businesses in every industry – healthcare, fintech, banking, the public sector, marketing, and more – to leverage machine learning and AI technology that was previously limited to organizations with vast resources at their disposal. By automating most of the manual modeling tasks that used to be necessary in order to develop and deploy machine learning models, automated machine learning enables business users to implement machine learning solutions with ease and frees up data scientists to focus on more complex problems.
Automated Machine Learning + DataRobot
DataRobot invented automated machine learning. Our world-class platform allows organizations of all sizes and business users of all skill levels to quickly and easily leverage the power of machine learning and AI to solve problems. With DataRobot, companies across industries have improved operations, increased customer retention, and identified key factors relevant to everything from loan default to the need for medical care.
DataRobot also offers classes through DataRobot University for anyone looking to either bring automated machine learning to their organization, take their machine learning prowess to the next level, or just learn how organizations can benefit from the technology. DataRobot’s automated machine learning platform is the key to the AI-driven enterprise.