On-Demand

All You Need to Know About Feature Stores

Take your models to the next level with ultimate fidelity and consistency across training and inference

Available on-demand

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

Speakers

Speaker-Cezar-Steinz-1660758008

Cezar Steinz

Manager of Machine Learning Operations

Via

Cezar Steinz leads the machine learning operations at Via, being responsible for the development of MAGI, the company’s internal ML Platform, which includes the Feature Store Balthazar. Previously, he worked at Avanade, developing projects for industry-leading enterprises such as Safra, Avianca, Natura, Bradesco, Bauducco, Insper and Ambev, and started his career at Atento working there for almost 10 years.

Speaker-Mani-Parkhe-1660758008

Mani Parkhe

Sr. Staff Software Engineer

Databricks

Mani Parkhe is an ML/AI Platform Engineer at Databricks, where he is currently the tech lead of the Feature Store team. Prior to this, he has worked on several customer-facing and open source platform initiatives for ML training, and deployment of ML models on the cloud and was the tech lead for the Databricks Model Registry team. He is a lifelong student with a passion for elegance in design and system architecture. After spending 15 years building software for semiconductor chip CAD, Mani transitioned to building big data infrastructure, distributed systems and web services, and machine learning. Prior to Databricks, he has worked on various data intensive batch and stream processing problems at LinkedIn and Uber. Mani has a Masters degree in CS from University of Florida. He lives in Almaden Valley with his wife and three amazing kids.