Featured Resource
Introducing the world’s first data science book for kids!
Florence the Data Scientist and Her Magical Bookmobile
Bring home a copyModel Monitoring Best Practices
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.
Organizing Enterprise Data Science
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. (Is a Data Science Center of Excellence, or COE, the right approach?) 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.
The Pros and Cons of Spark in a Modern Enterprise Analytics Stack
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.