As data engineers transform raw data into features, they often struggle to store, process and serve these features to machine learning pipelines. As data scientists train their ML models, they often want to explore and use features created by their peers across the organization. The Databricks Feature Store supports these use cases and more.
In this webinar, you’ll learn how to:
- Store, process and share features for your ML models
- Retrieve and reuse features across ML workflows
- Create and explore new features that are easily reproducible
- Use MLflow to maximize the accessibility of your features
- Cezar Steinz, Machine Learning Operations Manager, Via
- Mani Parkhe, Sr. Staff Software Engineer, Databricks