Note: Before you learn about automated machine learning, you should definitely be familiar with what standard machine learning is.

Only the biggest thing to hit the business world since the internet. Automated machine learning represents a fundamental shift in the way organizations of all shapes and sizes approach machine learning and data science.

Applying traditional machine learning methods to real-world business problems is extremely 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 literally all of that. It makes 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 in artificial intelligence (AI) jargon is often referred to as “the signal in the noise.” Automated machine learning incorporates machine learning best practices from top-ranked data scientists.

So where does the “automated” part actually come in? Here is what the standard machine learning process looks like, at a high level:



In the first process, the only automatic task is model training. We’ve taken the liberty of outlining in red which of the rest of the steps can be automated using an automated machine learning platform.

Now let’s take a look at the automated machine learning process itself, all of which happens automatically after you upload your dataset to the platform and choose what you want to predict.

Figure 2 zooms in on the parts of the machine learning process that are now automated, allowing for greater agility in problem-solving and democratizing data science to include those without extensive programming knowledge or other necessary resources inherent to the traditional machine learning process.


Developing a model with the traditional process, as you can see from Figure 1, takes weeks or months for just one model. With automated machine learning, it takes days at most to develop and compare dozens of models, resulting in better solutions in a fraction of the time. The bulk of the work is no longer spent in manual, tedious modeling tasks. Now, business users and data scientists can spend their time on refining and practically applying predictive models, allowing them to solve more problems much faster.

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 places where human error can rear its ugly head, getting in the way of model accuracy and devaluing the model’s predictions. 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 of every size in every industry – healthcare, fintech, banking, the public sector, marketing, you name it – to leverage machine learning and AI technology that was previously limited to those 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 predictive models, automated machine learning enables business users to implement machine learning solutions with ease and frees up data scientists to focus on more complex applications of predictive analytics.

Automated Machine Learning + DataRobot

It’s pretty simple: 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 predictive analytics 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.

Not only that, DataRobot 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.

We say it a lot, but that’s because we mean it: DataRobot’s automated machine learning platform is the key to the AI-driven enterprise.