![[WEBINAR] Building a Feature Store with Featureform and Sagemaker](/wp-content/uploads/webinar-building-a-feature-store-with-featureform-and-sagemaker-bfc454b.jpg)
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Join us for a 1-hr workshop where we'll build and deploy a model that runs on Featureform and Sagemaker!
By Shabnam MokhtaraniFollow
Date and time
Tuesday, November 21 · 8 – 9am PST
Location
Online
About this event
- 1 hour
- Mobile eTicket
Feature stores play a pivotal role in streamlining machine learning workflows for data scientists. They centralize and standardize feature engineering, ensuring consistent and reusable features across models. This not only accelerates model development and deployment but also enhances model accuracy and reproducibility. By addressing challenges like data fragmentation and inconsistency, feature stores optimize the ML lifecycle.
What We’ll Cover:
- Define and register datasets, transformations, features, and labels in Featureform
- Define feature pipelines that run on Sagemaker
- Train and deploy a model that utilizes both batch and on-demand features
What is Featureform:
Featureform is a virtual feature store that enables data scientists to define, manage, and serve their ML model’s features. It sits atop existing infrastructure, transforming it into a traditional feature store. By using Featureform, data science teams can enhance collaboration, organize experimentation, facilitate deployment, increase reliability, and ensure compliance. It allows for standardized definitions of transformations, features, labels, and training sets, making them easily shareable and understandable across teams. Additionally, Featureform is designed to work with both individual data scientists and large enterprise teams, providing a centralized repository for machine learning resources.
Check out our open-source Feature Store here: https://github.com/featureform/featureform
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