Universal Data Representation for AI

Joel Christner, (@joelchristner, Founder/CEO at @viewyourdata) discusses the complexities of data management in AI, structured and unstructured data, the importance of RAG pipelines and vector databases. 

SHOW SUMMARY:  Aaron and Joel discusses the complexities of data management in AI, focusing on the concept of universal data representation. They explore the challenges organizations face with structured and unstructured data, the importance of RAG pipelines and vector databases, and the implications of data privacy in regulated industries. The conversation also touches on managing model versions and the emerging patterns in AI tooling that can help enterprises effectively utilize AI technologies.

SHOW: 925

SHOW TRANSCRIPT: The Cloudcast #925 Transcript

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SHOW NOTES:

• View.io website

Topic 1 - Welcome to the show, Joel. Give everyone a quick introduction.

Topic 2 - Our topic today is everything data and how to represent it and embed it into AI systems. First, what is the challenge with data, structured or unstructured, in organizations today and what is behind the concept of Universal Data Representation

Topic 3 - Industry or customer specific data today is big challenge for organziations, especially in highly regulated industries such as healthcare, financial services, etc. The most prevalent solution I am seeing is taking an existing foundational model and then adding a RAG pipeline vs. the cost and time to fine tuning. What are you seeing?

Topic 4 - Even when companies have good data, that doesn’t mean that data makes it into the AI pipeline correctly, this is where the embedding problem and your concept of Universal Data Representation comes into play, correct?

Topic 5 - But, once you get the first model out, then what? How should the data and models be handled over time? How do you create a platform and a continuous feedback loop to improve the results over time?

Topic 6 - What are the most successful use cases you are seeing today with your customers?

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