DataRobot Blog

With growing pressure on data scientists, every organization needs to ensure that their teams are empowered with the right tools. At the same time, the toolkit needs to meet enterprise needs and regulatory requirements. Data science notebooks have become a crucial part of the data science practice. See the advantages and challenges different notebook solutions bring to the table.

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? 

DataRobot offers end-to-end explainability to make sure models are transparent at all stages of their lifecycle. In this post, we’ll walk you through DataRobot’s Explainable AI features in both our AutoML and MLOps products and use them to evaluate a model both pre- and post-deployment. 

The second part of the “Ever-growing Importance of MLOps” blog will dive deeper into DataRobot’s Machine Learning Operations capability, and its transformative effect on the machine learning lifecycle.  

When your employer adopts AI solutions, your work may be simplified in the long run, but implementing the new technologies may require some initial effort. Follow these guidelines to adapt to the changes.

AI and machine learning initiatives are the gifts that keep on giving, simultaneously increasing top-line revenue and decreasing bottom-line costs. But to meet this scale in demand, organizations have to navigate a myriad of new challenges, from IT governance and security, to data security, privacy, and tax regulatory compliance. And automation is the key to AI success. 

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.