DataRobot Blog

As AI has become more ubiquitous, we’ve started to see increasingly more examples of AI behaving badly. Recent high-profile examples include hiring models that are biased against women and facial recognition systems that can’t identify black individuals. At DataRobot, we want to ensure that users have the right tools at their disposal to investigate bias. To meet that goal, we…

True Story About Eureqa  The idea of computers helping to make scientific discoveries is incredibly fascinating to me. My advisor at Cornell (and advisor on Eureqa), Hod Lipson, once joked to me “maybe all the laws of physics are elegant and beautiful because that’s all that human minds can ever hope to understand.” Along with Hod and another brilliant educator,…

In several earlier blog posts, we have focused on what we at DataRobot call the AI production gap, which refers to the gap that makes it difficult to transition models from the data science teams who develop them to the IT and DevOps teams who are responsible for deploying and monitoring them in production. Deployment and Monitoring Challenges Our DataRobot…

Announced at our recent AI Experience ‘21 conference and now in Release 7.1, we are thrilled to introduce the new No-Code AI App Builder. This allows any user, regardless of their technical expertise, to quickly turn a deployed model into a rich AI application without requiring any coding. This means that any predictive model can become an AI app in…

When I offered recent podcast guest Cindi Howson the opinion that data science has become much simpler, she had a ready response: “Are you telling me it’s not hard anymore?” I had to laugh. But Howson knows her data science. Now the chief data strategy officer at the company ThoughtSpot and host of the podcast The Data Chief, Howson has…

Freshly cut grass, coffee, gasoline, cinnamon: all of these are very distinctive smells. To create the most alluring scents, the creators of fragrances have long relied on perfumers with a “good nose.” But those perfumers now have help from AI. AI is capable of predicting the scents based on the molecular composition of a product. This post shows you how…

Although the fields of machine learning and AI have begun to mature, there is often a lack of success in applying AI in organizations, an issue DataRobot calls the "AI production gap." One of the leading common issues we see in the machine learning life cycle (feature engineering, training, scoring, etc.) is the involvement of a variety of separate and…

I started a family late in life. I have a three-year-old son and just a couple of months ago, celebrated my fifth wedding anniversary. There are pluses and minuses to starting late. My lifestyle choices don’t neatly fit into customer categories that marketers have been using for decades. Some marketing campaigns assume that if I’m a fifty-something man purchasing a…