Building High Performance Recommender Systems with Feature Stores | Tecton
Slides: https://www.datacouncil.ai/talks/building-high-performance-recommender-systems-with-feature-stores?hsLang=en
ABOUT THE TALK:
Recommender systems are highly prevalent in modern applications and services but are notoriously difficult to build and maintain. Organizations face challenges such as complex data dependencies, data leakage, and frequently changing data/models. These challenges are compounded when building, deploying, and maintaining ML pipelines spans data scientists and engineers. Feature stores help address many of the operational challenges associated with recommender systems.
In this talk, we explore:
Challenges of building recommender systems
Technical and organizational challenges feature stores solve
How to integrate Feast, an open-source feature store, into an existing recommender system to support production systems
ABOUT THE SPEAKKER:
Danny Chiao is an engineering lead at Tecton/Feast Inc working on building a next-generation feature store. Previously, Danny was a technical lead at Google working on end to end machine learning problems within Google Workspace, helping build privacy-aware ML platforms / data pipelines and working with research and product teams to deliver large-scale ML powered enterprise functionality. Danny holds a Bachelor’s degree in Computer Science from MIT.
ABOUT DATA COUNCIL:
Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers. Make sure to subscribe to our channel for more videos, including DC_THURS, our series of live online interviews with leading data professionals from top open source projects and startups.
FOLLOW DATA COUNCIL:
Twitter: https://twitter.com/DataCouncilAI
LinkedIn: https://www.linkedin.com/company/datacouncil-ai/
Eventbrite: https://www.eventbrite.com/o/data-council-30357384520
Видео Building High Performance Recommender Systems with Feature Stores | Tecton канала Data Council
ABOUT THE TALK:
Recommender systems are highly prevalent in modern applications and services but are notoriously difficult to build and maintain. Organizations face challenges such as complex data dependencies, data leakage, and frequently changing data/models. These challenges are compounded when building, deploying, and maintaining ML pipelines spans data scientists and engineers. Feature stores help address many of the operational challenges associated with recommender systems.
In this talk, we explore:
Challenges of building recommender systems
Technical and organizational challenges feature stores solve
How to integrate Feast, an open-source feature store, into an existing recommender system to support production systems
ABOUT THE SPEAKKER:
Danny Chiao is an engineering lead at Tecton/Feast Inc working on building a next-generation feature store. Previously, Danny was a technical lead at Google working on end to end machine learning problems within Google Workspace, helping build privacy-aware ML platforms / data pipelines and working with research and product teams to deliver large-scale ML powered enterprise functionality. Danny holds a Bachelor’s degree in Computer Science from MIT.
ABOUT DATA COUNCIL:
Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers. Make sure to subscribe to our channel for more videos, including DC_THURS, our series of live online interviews with leading data professionals from top open source projects and startups.
FOLLOW DATA COUNCIL:
Twitter: https://twitter.com/DataCouncilAI
LinkedIn: https://www.linkedin.com/company/datacouncil-ai/
Eventbrite: https://www.eventbrite.com/o/data-council-30357384520
Видео Building High Performance Recommender Systems with Feature Stores | Tecton канала Data Council
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