Staying in tune with consumer demands can be challenging, especially when these demands change almost daily. Tracking consumption patterns to predict demand is a monumental task. Previously, solving this problem involved spreadsheets or legacy statistical methods. But with automation and machine learning, artificial intelligence can help organizations slice through mounds of data to get more accurate and timely insights on consumer demand . Machine learning can build forecasts for hundreds of thousands of items and consider all of the minute details for each one of them, from seasonality to sales history. For example, automated machine learning can easily create separate models on clusters of products, using unsupervised ML techniques, to make predictions even more granular such as by store, week, and SKU.

This year's 72nd Primetime Emmy Awards will take place on Sunday, September 20th. Looking back at last year's post where we used DataRobot to predict the winners for the 2019 Primetime Emmy Awards for the "Outstanding Drama Series" and "Outstanding Comedy Series" categories, we correctly predicted Game Of Thrones winning Outstanding Drama Series but missed the mark on Outstanding Comedy Series (Fleabag was the winner, whereas we predicted Veep would take the top prize). Since Game Of Thrones is absent from this year's 2020 Emmy nominations, let's see if the data says anything about potential underdog winners!

Humble AI is a new feature in DataRobot that protects the quality of your predictions in situations where the model may be less confident. With Humble AI, users create rules for models in deployment for predictions made in real-time. These rules identify conditions that indicate quantifiably that a prediction may be unsure, and then can trigger actions like defaulting to…

The first three episodes of More Intelligent Tomorrow, an exploratory AI podcast featuring conversations with experts and thought leaders across industries, are live. You can find the full-length audio wherever you listen to podcasts and the shorter video versions here on our site. The initial conversations with these AI movers and shakers have brought up a wide array of topics:…

Cutting-edge algorithms and new research will continue to drive the advancement of machine learning. However, there’s a more straightforward way of resolving many challenges in machine learning today, especially when it comes to ethics and better alignment with human values. And it is not surprisingly, not focused on the technology, but rather on the people using it. For advanced AI and machine learning systems already in production, the focus is on delivering the intended value of the system, which is no longer a question of leveling up the technology or mathematical techniques behind it. Value-oriented approaches can be supported holistically by both the human expertise involved in creating AI and by the technology itself.

As someone who spends hours searching for new music, getting lost in rabbit holes of ‘related artists’ or ‘you may also like’ tabs, I wanted to see if cover art improves the efficiency of the search process. Browsing through a collection of images takes a lot less time than listening to clips of songs. As a starting place, I was curious if machine learning could accurately predict an album's genre from the cover art. In this post, I will show you how I built this model and what it teaches us about the role a record’s cover plays in categorizing and placing an artist's work into a musical context.

With AI proving to be the most transformative technology of our time and companies today need to pivot faster and drive near-term impact from their tech investments, many organizations are looking to drive higher ROI from AI as quickly as possible. From predicting customer churn to reducing fraud and avoiding supply chain disruptions, the possibilities are virtually limitless as to how…

I started interning at DataRobot on May 11th. As a student, I haven't had much experience with real-world applications of data science. Many of the courses I have taken are theory-based mathematics or programming, which has helped me understand the modeling methods and statistical analysis DataRobot automates but not so much applying it to business problems. In other words, when…