MLOps.community Unpacking 3 Types of Feature Stores // Simba Khadder // #265
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Oct 1, 2024 Simba Khadder, the founder and CEO of Featureform and a machine learning expert, dives deep into the evolution of feature stores and their intersection with vector stores. He explains the significance of embeddings for recommender systems and discusses how personalization enhances user experiences with large language models. Simba also addresses the challenges in managing feature pipelines and the trade-offs between system complexity and reliability. Tune in to learn about the latest innovations shaping the MLOps landscape!
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Embeddings in Recommender Systems and LLMs
- Embeddings power recommender systems and LLMs by representing users and items holistically.
- This allows models to understand complex relationships and analogies from sparse data, like user-item interactions.
Coke Embeddings Analogy
- Simba Khadder found a model derived an analogy between Coke, Diet Coke, Cherry Coke, and Coke Zero.
- This demonstrates how embeddings can capture complex relationships just from user purchase data.
Deep Learning vs. Traditional Models
- University often overemphasizes deep learning, creating a gap between education and industry practices.
- Many companies still rely on simpler models like XGBoost due to data limitations and practical considerations.