There is significant industry need for synthetic data to support testing, validation, demos, training scenarios and more. In this course, Creating Synthetic Datasets with Generative AI, you’ll gain the ability to effectively use generative AI to create professional-level synthetic datasets. First, you’ll explore assessing the true needs, parameters, and properties of a valid dataset. Next, you’ll discover how to leverage generative AI to produce these datasets, complete with bounds, data integrity, and even intentional errors common to real world datasets. Finally, you’ll see how prompt engineering best practices apply to iterate and refine your datasets. When you’re finished with this course, you’ll have the skills and knowledge needed to more confidently generate datasets with generative AI and be able to effectively leverage this skill in real-world settings.
Author Name: Russ Thomas
Author Description:
Currently an IT leader in Denver Colorado’s FinTech sector Russ has focused on database engineering, modelling, administration, and BI since 1997. Russ is a passionate trainer and community volunteer presenting regularly at tech events and local user groups around the US. In 2019 much of Russ’ focus turned to cloud based data lakes specifically on Google BigQuery, and the emerging world of large language models.