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.
While the necessity to embed AI into the business is clear, the road to get there isn’t. One question many data science leaders wrestle with is how to organize data science teams to achieve the greatest impact. In our conversations with nearly a dozen industry leaders building model-driven businesses, we found that there’s no one-size-fits-all answer. In this report, we break down best practices for enterprise data science across three areas (discipline, process, technology) that these leaders of high-performing global data science teams shared with us. Whether you’re early in your journey or well underway and seeking to strengthen the impact of existing efforts, their insights can help you chart the right course for your organization.
This paper highlights how the Domino data science platform addresses governance, security, infrastructure monitoring, and other important criteria during an IT evaluation of data science solutions.
One of our most popular resources, this guide shares lessons from the field on managing data science projects and portfolios.
Spark is a distributed computing framework that has skyrocketed in popularity over the last several years for data engineering and analytics use cases. This paper provides a brief overview of Spark’s strengths and weaknesses in the context of data science and machine learning workflows.
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.
In this technical webinar, we address the key challenges every data science team faces when training and operationalising complex AI models at scale. Nikolay and Adam will share how Domino Data Lab facilitates knowledge discovery and collaboration in teams, enabling data scientists to use their favourite tools with a lightning-fast...
Learn how global pharmaceutical research leader Janssen Research & Development has accelerated model training on multi-GPU machines, allowing them to more quickly and accurately diagnose and characterize cancer cells through whole-slide image analysis.
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.