Simba Khadder is the Founder & CEO of Featureform. He started his ML career in recommender systems, where he architected a multi-modal personalization engine that powered 100s of millions of users’ experiences.

Unpacking 3 Types of Feature Stores // MLOps Podcast #265 with Simba Khadder, Founder & CEO of Featureform.

// Abstract

Simba dives into how feature stores have evolved and how they now intersect with vector stores, especially in the world of machine learning and LLMs. He breaks down what embeddings are, how they power recommender systems, and why personalization is key to improving LLM prompts. Simba also sheds light on the difference between feature and vector stores, explaining how each plays its part in making ML workflows smoother. Plus, we get into the latest challenges and cool innovations happening in MLOps.

// Bio

Simba Khadder is the Founder & CEO of Featureform. After leaving Google, Simba founded his first company, TritonML. His startup grew quickly, and Simba and his team built ML infrastructure that handled over 100M monthly active users. He instilled his learnings into Featureform’s virtual feature store. Featureform turns your existing infrastructure into a Feature Store. He’s also an avid surfer, a mixed martial artist, a published astrophysicist for his work on finding Planet 9, and he ran the SF marathon in basketball shoes.

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Website: featureform.com

BigQuery Feature Store // Nicolas Mauti // MLOps Podcast #255: https://www.youtube.com/watch?v=NtDKbGyRHXQ&ab_channel=MLOps.community

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Timestamps:

[00:00] Simba's preferred coffee

[00:08] Takeaways

[02:01] Coining the term 'Embedding'

[07:10] Dual Tower Recommender System

[10:06] Complexity vs Reliability in AI

[12:39] Vector Stores and Feature Stores

[17:56] Value of Data Scientists

[20:27] Scalability vs Quick Solutions

[23:07] MLOps vs LLMOps Debate

[24:12] Feature Stores' current landscape

[32:02] ML lifecycle challenges and tools

[36:16] Feature Stores bundling impact

[42:13] Feature Stores and BigQuery

[47:42] Virtual vs Literal Feature Store

[50:13] Hadoop Community Challenges

[52:46] LLM data lifecycle challenges

[56:30] Personalization in prompting usage

[59:09] Contextualizing company variables

[1:03:10] DSPy framework adoption insights

[1:05:25] Wrap up