DataRobot Blog

At DataRobot, we meticulously test all the models we build on thousands of real-world datasets to ensure they meet our customers high expectations across performance, generalization, and reliability. There’s no denying that tree-based methods — especially boosting models — have dominated our leaderboards when it comes to traditional data types in rows and columns, aka tabular data. This has begun…

Visual AI is New in DataRobot 6.0 In Release 6.0 of DataRobot, we are thrilled to announce a ground-breaking new capability in our Automated Machine Learning product. DataRobot Visual AI gives you the ability to easily incorporate image data into your machine learning models alongside tabular and text-based data types. This enables your organization to get value from computer vision,…

Data is the fuel that drives high-scale innovation with AI. Enterprises that put a strategy in place encouraging a culture of collaboration and sharing of their data assets will benefit the most. These organizations will see exponential gains in productivity with AI. Their employees will work together on projects, sharing their ideas, things they create, and their domain expertise.  However,…

The truth is that the work of data scientists cannot generate value if the models never make it to production. For data scientists writing custom models in languages like Python and R, the number of challenges for getting models into production can be overwhelming. Issues range from how to deploy model code on production systems, how to monitor performance, and…