From MVP to Production // Day 2 Panel 2 // AI in Production Conference

Knowledge Update

Ensuring AI systems remain current with the latest information is a significant challenge. Alex Volkov highlights the issue of knowledge cutoffs in AI models, which can lead to outdated information if not regularly updated 1. Andrew Hoh describes the complexity of version control in maintaining up-to-date knowledge, noting that it's akin to managing recommendation systems where infrastructure, data sources, and processing pipelines must be meticulously versioned 1.

It's like a massive version control problem. We extracted as much out as possible to try to version control everything.

--- Andrew Hoh

This meticulous versioning is crucial to prevent performance degradation and ensure the AI model's accuracy and reliability.

   

Version Control

Data version control is essential to avoid model degradation over time. Andrew Hoh emphasizes the need for systematic approaches to manage data versions, likening it to the challenges faced in traditional machine learning systems 1. He points out that without proper version control, rolling back to previous versions can become a painful process, highlighting the importance of pinning versions to maintain consistency 1.

There's no way to roll back because it's going to be so painful.

--- Andrew Hoh

This approach ensures that any updates or changes do not inadvertently affect the model's performance, maintaining its effectiveness over time.