Building high-quality AI models entails an ongoing cycle of model training, validation, refinement, and monitoring in the face of continually evolving data. “Real datasets always have lots of data biases that confuse models. It is painstakingly difficult to find and fix these issues!” and “It takes us weeks to get to the root cause of systematic model failures.” are illustrative quotes from machine learning practitioners who've experienced the pivotal importance of data and model quality firsthand.

The crux of the issue? The manual nature of this process becomes unmanageable as AI datasets and models expand in size and complexity. In this talk, we explore how LatticeFlow empowers ML teams to achieve the delivery of resilient and high-performance AI models through a combination of data and model diagnosis and enhancement.

The wide range of scenario and edge cases to account for makes ingesting and processing unstructured data into vector databases difficult. You can offload some of the complexity by using a vector embedding pipeline. This, in combination with an automated evaluation system, will allow you to experiment with different ingestion techniques to see what works best for your data and use-case.

Check their open source repo - https://github.com/dgarnitz/vectorflow