Advertisement

Data Science: How to Shift Toward More Transparency in Statistical Practice

Data Science and statistics both benefit from transparency, openness to alternative interpretations of data, and acknowledging uncertainty. The adoption of transparency is further supported by important ethical considerations like communalism, universalism, disinterestedness, and organized skepticism.  Promoting transparency is possible through seven statistical procedures:  Data visualization Quantifying inferential uncertainty Assessment of data preprocessing choices Reporting multiple models Involving […]

Integrated Deployment – Deploying an AutoML Application with Guided Analytics

Welcome to our collection of articles on the topic of integrated deployment, where we focus on solving the challenges around productionizing Data Science. So far, in this collection we have introduced the topic of integrated deployment, discussed the topics of continuous deployment and automated machine learning, and presented the autoML verified component.  In today’s article, we would like to […]

3 Strategies for Creating a Successful MLOps Environment

Disconnects between development, operations, data engineers, and data science teams might be holding your organization back from extracting value from its artificial intelligence (AI) and machine learning (ML) processes. In short, you may be missing the most essential ingredient of a successful MLOps environment: collaboration. For instance, your data scientists might be using tools like JupyterHub or […]

Using Big Data Analytics to Combat White-Collar Crime

In the era of globalized markets, burgeoning international trade, complex financial systems, ever-evolving compliance and regulatory landscapes, and rapid technology advancement, white-collar crime has unfortunately seen a significant uptick in scale, variety, and sophistication. Whereas white-collar crime used to conjure images of high-flying executives stealing from company coffers, the modern landscape is much more complex, […]