DataRobot Blog

For the past decade, I’ve used the data of past tournaments to build a machine learning model for completing March Mania brackets.

DataRobot and Snowflake Jointly Unleash Human and Machine Intelligence Across the Industrial Enterprise Landscape. Read more in the blog post.

Model risk refers to the inherent risks associated with running machine learning models in production. Read this post to learn how to manage model risk.

Integrated care is seamless patient care to the point where patients don't feel friction when working with different provider or payer entities, no matter how many are engaged in their care. So, how do we make it a reality? 

Time series projects are among the most challenging in the world of data science today. But for organizations striving to become AI-driven, understanding how to approach those projects is essential.

Through our Snowflake and DataRobot partnership, we are offering the world’s most advanced healthcare transformation platform that’s fully cloud-native to tackle some of the most complex challenges ever faced by the NHS.

It’s safe to say that most data science fails are inadvertent rather than malicious. A deep dive case study into several high profile AI failures revealed a common narrative: people meant well but they didn’t stop to think what could go wrong. Their lack of AI governance inevitably led to embarrassing failures.

DataRobot offers a visual and intuitive UI that enables anyone to explore and prepare data for machine learning. It also automates many of the mundane, routine data science tasks that previously required coding and weeks or months of effort.

AI has been making front page news and not always for the right reasons. High profile AI failures have included sexist hiring algorithms and racist healthcare programs. But AI failures aren’t limited to ethical lapses. How can we avoid AI impact statements becoming another administrative compliance burden?