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 ...

Turning his attention to the extremely time-consuming task of machine learning data preparation, Dr. James McCaffrey of Microsoft Research explains how to examine data files and how to identify and ...

Amazon today announced it has extended its program for data cleansing, known as Glue, with a visual user interface that automates some steps necessary to prepare data, to simplify the task for ...

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 ...

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 ...

Data preparation is an important step in any data analysis. This article offers suggestions for making that process easier and more effective. TechRepublic Get the web's best business technology news, ...

These next data preparation steps will be explained in future VSM Data Science Lab articles. When starting out on a machine learning project, there are ten key things to remember: 1.) data preparation ...