DataRobot Blog

We’re excited to announce our partnership with Labelbox, the leading provider of unstructured data labeling capabilities. Labelbox’s technology reduces the time required to label complex datasets by 5-10 times, allowing a small team to no longer need to iterate for months to deliver accurate training data for high model performance.

To make laser-sharp predictions, McLaren’s Formula 1 drivers and team members use DataRobot automated time series capabilities to forecast air and track temperatures.

The stakes in managing model risk are at an all-time high, but luckily automated machine learning provides an effective way to reduce these risks. Learn more.

Part 2 in a series on the emerging practice of "green AI." In this blog, the focus is on actionable ways AI practitioners can build their models to minimize the cost of Red AI.

Find out how to approach machine learning projects in a sustainable and cost-efficient manner by reassessing the efficiency metrics applies to models.

Another year at HIMSS has come and gone and DataRobot remains as energized as ever! This year was a big moment for the DataRobot team. We debuted our growing healthcare organization, offered a preview of the AI Cloud DataRobot 8.0 release, and had hundreds of conversations surrounding the impact of AI and the substantial support for the healthcare industry.

With DataRobot, we can modernize our approach of forecasting solar irradiance, use these models to optimize solar power generation, and contribute to the clean energy revolution across the globe.

AI impact assessments are more than black and white compliance documents. Read more about AI Impact Statements - empathy, imperfection, and responsibility.

We met with Randy Singh, Director of Strategy and Sporting at McLaren Racing, to see how they look at data and identify what matters most.