Too often, companies run models in production without adequately managing the risk of model drift. Or to manage it, they rely on data scientists doing manual and time-consuming work, distracting resources from future research and innovation. This whitepaper describes the common reasons and types of drift, and provides an overview of best practices for mitigating the risk of drift and monitoring to detect drift early.
Read Gartner’s report for insights and recommendations that gathered through a variety of customer interactions on the critical capabilities for data science and machine learning platforms.
This paper highlights how Domino data science platform addresses some of the most important topics assessed during an evaluation of a data science platform solutions.
Learn 4 milestones of digital transformation for health and life sciences organizations, 6 common challenges to reaching those milestones, and best practices that high-performing research and data science teams have adopted for dismantling these challenges.
Watch this webinar, hosted by Domino's Samit Thange and Bob Laurent, to learn more about the factors that can cause model performance to decrease, as well as some of the leading indicators to predict when it's time to re-train or re-build a model.
Join this webinar to experience new capabilities like on-demand Spark clusters, enhanced project management with Jira integration, ability to export models to Amazon SageMaker and Microsoft AKS certification first-hand via live demos and discussion.