Data preparation can be complicated. Get an overview of common data preparation tasks like transforming data, splitting datasets and merging multiple data sources. Image: Artem/Adobe Stock Data ...

For design engineers, an artificial intelligence (AI) workflow encompasses four steps: data preparation, modeling, simulation and testing, and deployment. While all steps are important, many engineers ...

We live in a data-rich world where information is ours for the taking. But throwing just any data at your algorithm is a bad idea. With AI, small inconsistencies quickly become big ones. And those ...

Machine learning, or ML, is growing in importance for enterprises that want to use their data to improve their customer experience, develop better products and more. But before an enterprise can make ...

Machine learning workloads require large datasets, while machine learning workflows require high data throughput. We can optimize the data pipeline to achieve both. Machine learning (ML) workloads ...

A new study published in Global Ecology and Biogeography presents a step-by-step guide to compile numerous fossil pollen datasets into a user-specific, standardized and clean compilation—ready for ...

Imagine this: you’ve just received a dataset for an urgent project. At first glance, it’s a mess—duplicate entries, missing values, inconsistent formats, and columns that don’t make sense. You know ...

SAN FRANCISCO, Calif., Sept. 20 — Trifacta, the global leader in data wrangling, today announced the release of Trifacta v4. The latest release expands upon Trifacta’s award-winning approach to data ...