Monday, June 3, 2024 - 1:00pm to 4:30pm

A Full Technical Breakdown of Data Generation with AI Models

All successful AI projects start and end with the data. The problem? Not everyone has the data required to build AI models in a production setting. Dataset generation has grown significantly with the rise of generative AI, making it easy for anyone to get started with training models no matter how much of your own data you bring to the table. While this sounds great, there are a ton of variables that go into this process of successfully generating data for training. How much data is needed? What models should be used to generate data? How to prompt models to generate high-variance datasets that actually improve results? What use cases make sense? Join Matt Payne as he dives deep into how to successfully build data generation pipelines that allow you to train new AI models. Discover how easily non-AI users can get started with a number of tools that make this process easy. Want to dive into complex use cases as an AI expert? Join Matt as he walks through real production use cases which have had success within popular domains.

Matt Payne

Matt Payne is a former Capital One ML Engineer and is the Founder and CEO of is an AI consulting & development firm focused on building AI-based applications for enterprise & early stage startup clients. is a current leader in building and consulting on production-grade GPT products and Matt has authored a number of whitepapers and technical reviews on using this state-of-the-art resource. Matt's focus is predominently on key areas of customer improvement including: prompt engineering, AI model fine-tuning, and early stage AI strategy.