IBM Synthetic Data Sets
Book Details
| Authors | Erik Altman, Dipali Aphale, Joy Deng, Yadu Nandan B, Saurabh Srivastava, Kelly Xiang |
| Publisher | Redbooks |
| Published | 2025 |
| Edition | 1st |
| Paperback | 38 pages |
| Language | English |
| ISBN-13 | 9780738461991 |
| ISBN-10 | 0738461997 |
| License | Open Access |
Book Description
IBM Synthetic Data Sets is a family of artificially generated, enterprise-grade datasets that enhance predictive artificial intelligence (AI) model training and large language models (LLMs) to benefit IBM Z and IBM LinuxONE clients, ecosystems, and independent software vendors. These pre-built datasets are downloadable and packaged as comma-separated values (CSVs) and data definition language (DDL) files, making them familiar to use, and compatible with everything from databases to spreadsheets to hardware platforms to standard AI tools. These datasets also leverage the IBM industry expertise and domain knowledge of the financial services sector without using any real client seed data, which alleviates security concerns with Personally Identifiable Information (PII). Real data at client sites is often limited in scope to only their own organization's transactions, and clients do not always know which transactions are fraudulent or not. To address this scenario, IBM Synthetic Data Sets were modified for fraud detection use cases so that clients can download and enable development of predictive AI models and LLMs for financial services or optimize existing models for improved accuracy and risk mitigation.
The IBM Synthetic Data Sets family contains the following features:
- IBM Synthetic Data Sets for Payment Cards
- IBM Synthetic Data Sets for Core Banking and Money Laundering
- IBM Synthetic Data Sets for Homeowners Insurance
This IBM Redbooks publication introduces IBM Synthetic Data Sets and provides information about how IBM Synthetic Data Sets can enhance and optimize your predictive AI model training and LLMs.
This book is published as open-access, which means it is freely available to read, download, and share without restrictions.
If you enjoyed the book and would like to support the author, you can purchase a printed copy (hardcover or paperback) from official retailers.
Download and Read Links
Share this Book
[localhost]# find . -name "*Similar_Books*"
Accelerating AI with Synthetic Data
Recently, data scientists have found effective methods to generate high-quality synthetic data. That's good news for companies seeking large amounts of data to train and build artificial intelligence and machine learning models. This report provides an overview of synthetic data generation that not only focuses on business value and use cases but a
OpenIntro Statistics, 4th Edition
OpenIntro Statistics provides a traditional college-level introduction to the field of statistics. This widely adopted textbook offers an exceptional and accessible foundation for a diverse range of students, from those at community colleges to attendees of Ivy League institutions. It is estimated that approximately 20,000 students use this thoroug
Critical Data Literacy
A short course for students to increase their proficiency in analyzing and interpreting data visualizations. By completing this short course students will be able to explain the importance of data literacy, identify data visualization issues in order to improve their own skills in data story-telling. The intended outcome of this course is to help s
Data Science at the Command Line, 2nd Edition
This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed wit
Introduction to Data Science
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data vi
R for Data Science
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data