Model Blind Spot Discovery for Better Models // Pavol Bielik & David Garnitz // Mini Summit #2
Skills:
RAG Basics80%
MLOps Community Mini Summit #2! Last week, we talked to Pavol Bielik, CTO of LatticeFlow, and David Garnitz, CTO of VectorFlow, hosted by Ben Epstein, brought to us by @latticeflow.
//Abstract
Ingesting Chaos
Handling Unstructured Data Reliably at Scale for RAG & Beyond: The wide range of scenarios 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.
Beyond Model Accuracy: How Model Blind Spot Discovery Helps to Build Better Models
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.
// Bio
Pavol Bielik
Pavol earned his PhD at ETH Zurich, specializing in machine learning, symbolic AI, synthesis, and programming languages. His groundbreaking research earned him the prestigious Facebook Fellowship in 2017, representing the sole European recipient, along with the Romberg Grant in 2016.
Following his doctorate, Pavol's passion for ensuring the safety and reliability of deep learning models led
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