When faced with insufficient data for training machine learning models, one solution is to generate synthetic data. Contemporary advancements, such as Gen AI and other machine learning algorithms, have simplified this once intricate task. The table below delineates various types of generated data along with their distinctions.
Let’s examine an instance of generating synthetic data with ChatGPT (3.5). I aimed to create sales data for a TV shop and utilized the provided prompt to generate synthetic data, outlining specific details for each column. The provided details serve as metadata for the file. It’s worth noting that tailored metadata can be generated by offering appropriate prompts that align with the specific context.
The generated output conveniently provides executable code for creating the desired CSV file in your local code editor. This process is not only quick and easy but also highly scalable, making it an efficient solution for generating fully synthetic data.
For those seeking partial or hybrid synthetic datasets, various options are available. Utilizing tools like GenAI, employing deep learning algorithms such as VAE and GAN, or leveraging external paid services like GenRocket, MDClone, Ydata, Mostly AI, among others, allows for the creation of tailored datasets to meet specific requirements.
In an next blog post, we will delve deeper into exploring the diverse applications of synthetic data.
