ChatGPT and other generative AI tools that create content are capturing a lot of attention. But content is only one application of generative AI. It can generate just about anything in terms of data.
Getting AI and machine learning projects off the ground remains a significant hurdle for many businesses. One of the biggest obstacles companies face is access to the datasets they need to properly build and scale their machine learning models.
Let’s say you work at a financial institution and you want to use machine learning to improve customer service or help introduce cross-sell and up-sell opportunities. Because of the sensitive nature of customers’ financial information, training the tech on real data isn’t a good idea.
RELATED EPISODE: How to Market to Finance Roles During Economic Uncertainty
That’s where synthetic data comes into play. MOSTLY AI generates synthetic data that looks and acts just like an organization’s real data. It can be used to train machine learning models, so they can work with seemingly real data without introducing additional risk.
On this episode of the B2B Nation podcast, we’re talking to Sabine Klisch, VP, Global Marketing at MOSTLY AI about the market for synthetic data, challenges, and goals for the year ahead.
2:13: Sabine explains the concept of synthetic data and why it’s important to MOSTLY’s audience.
4:42: What is the most effective message for MOSTLY’s target audience?
8:08: What are the goals Sabine has set for herself, her team, and her brand in 2023?
10:16: What are the challenges Sabine expects to face as 2023 continues?
12:34: How the popularity of ChatGPT helps raise the profile of all applications of generative AI.
15:23: What is Sabine’s favorite tool?