The use of synthetic data is not exactly a new practice: it has been a productive approach for several years, providing professionals with the data they need for their projects in situations where real-world data sets are inaccessible, not available. available or are limited by copyright or approved copyrights. -use perspective.
However, the recent rise of LLMs and ai-generated tools has transformed the synthetic data scene, as have many other workflows for machine learning and data science professionals. This week, we present a collection of recent articles covering the latest trends and possibilities you should know about, as well as the questions and considerations you should keep in mind if you decide to create your own toy data set from scratch. Let's dive in!
- How to Use Generative ai and Python to Create Designer Dummy Data Sets
If it's been a while since you last needed synthetic data, don't miss out. Mia DwyerThe concise tutorial from, which describes a simplified method for creating a dummy data set with GPT-4 and a bit of Python. Mia keeps things quite simple and you can adapt and develop this approach to suit your specific needs. - Creating Synthetic User Research: Using Personal Prompts and Autonomous Agents
For a more advanced use case that also relies on the power of generative ai applications, we recommend getting up to speed with Vicente KocGuide to synthetic user research. It leverages an autonomous agent architecture to “create and interact with digital customer personas in simulated research scenarios,” making user research more accessible and less resource-intensive. - Synthetic data: the good, the bad and the messy
Working with generated data solves some common problems, but may introduce some others. Tea mustac focuses on a promising use case: training ai products, which often requires massive amounts of data, and discusses the legal and ethical concerns that synthetic data can help us avoid, as well as those it cannot.
- Simulated data, real learning: scenario analysis
In his ongoing series, Jarom Hulet looks at the different ways simulated data can allow us to make better business and policy decisions and extract valuable insights along the way. After covering model testing and power analysis in previous articles, the latest installment focuses on the possibility of simulating more complex scenarios to obtain optimized results. - Synthetic data evaluation: the million-dollar question
The main assumption behind any process that relies on synthetic data is that the latter sufficiently resemble the statistical properties and patterns of the real data it emulates. Dr. Andrew Skabar offers a detailed guide to help practitioners evaluate the quality of their generated data sets and the degree to which they meet that crucial threshold.
