Explore a comprehensive podcast episode featuring Simba Khadder, Founder & CEO of Featureform, as he delves into the evolution of feature stores and their intersection with vector stores in machine learning and LLMs. Learn about embeddings, recommender systems, and the importance of personalization in improving LLM prompts. Gain insights into the distinctions between feature and vector stores, and their roles in streamlining ML workflows. Discover the latest challenges and innovations in MLOps, including discussions on scalability, the MLOps vs LLMOps debate, and the impact of feature store bundling. Understand the complexities of ML lifecycle challenges, the adoption of frameworks like DSPy, and the future of data science in AI development.
Unpacking Three Types of Feature Stores - MLOps Podcast
Overview
Syllabus
[] Simba's preferred coffee
[] Takeaways
[] Coining the term 'Embedding'
[] Dual Tower Recommender System
[] Complexity vs Reliability in AI
[] Vector Stores and Feature Stores
[] Value of Data Scientists
[] Scalability vs Quick Solutions
[] MLOps vs LLMOps Debate
[] Feature Stores' current landscape
[] ML lifecycle challenges and tools
[] Feature Stores bundling impact
[] Feature Stores and BigQuery
[] Virtual vs Literal Feature Store
[] Hadoop Community Challenges
[] LLM data lifecycle challenges
[] Personalization in prompting usage
[] Contextualizing company variables
[] DSPy framework adoption insights
[] Wrap up
Taught by
MLOps.community
Related Courses
-
From Robotics to Recommender Systems - MLOps Podcast #240
-
RecSys at Spotify - Building and Evaluating Large-Scale Recommender Systems
-
MLOps Build or Buy - Startup vs. Enterprise Perspectives
-
Information Retrieval and Relevance: Vector Embeddings for Semantic Search
-
Unified MLOps: Feature Stores and Model Deployment
-
BigQuery as a Feature Store for AI/ML Applications
