04-23, 16:00–16:30 (Europe/Berlin), B05-B06
Feature Stores have become an important component of the machine learning lifecycle. They have been particularly pivotal in bridging the gap between data engineering and machine learning workflows(experimentation, training and serving). This talk will explore Feature Stores with a focus on their evolution, what they look like now and what they could look like in the future with the advent of the AI ACT.
In recent years, the role of feature stores has become increasingly pivotal in data engineering and machine learning. This talk will delve into the history of feature stores, exploring their evolution from Uber's Michelangelo to recent solutions like Feast, Hopsworks and Fennel. Lastly, we will discuss the potential impact of the AI Act on the future of feature stores, highlighting regulatory constraints that may affect what they look like in the future.
The outline of this talk is detailed below.
Historical Perspective:
- Tracing the origins of Feature Stores: How did the concept evolve over time?
- Early use cases and challenges: Lessons learned from Michelangelo.
- Pioneering Feature Stores: Case studies on organizations at the forefront of adoption.
Current Landscape:
- Architectural insights: What do modern Feature Stores look like?
- Integration with popular ML frameworks and data storage solutions.
- Real-world success stories: How Zalando built a central Feature Store for serving features across departments and business units with different technical requirements.
AI ACT and the Future of Feature Stores:
- Envisioning Feature Stores in an AI ACT environment.
- Federated learning and distributed feature stores: Opportunities and challenges.
Novice
Expected audience expertise: Python –Novice
Abstract as a tweet (X) or toot (Mastodon) –Feature Stores have become an important component of the machine learning lifecycle. They have been particularly pivotal in bridging the gap between data engineering and machine learning workflows(experimentation, training and serving). This talk will explore Feature Stores with
I'm an Engineering Manager in the Machine Learning Platform team in Zalando where I build tools to make it easier for ML engineers, researchers and data scientists to be more productive and compliant.