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Join us for a 1-hour workshop where we’ll build a fraud detection model with Featureform’s Python API that runs locally on your laptop!
By Shabnam MokhtaraniFollow
Date and time
Thursday, September 28 · 12 – 1pm PDT
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.
In this one-hour workshop, we’ll show you the benefits of using a Feature Store without having to deploy one. With Featureform Local Mode, we can setup a feature store that runs locally on your laptop- perfect for a test run!
What We’ll Cover:
What You’ll Need:
- Python 3.7+ Installed
- Jupyter Notebook and Git*
* If you don’t have a Jupyter installed, you’ll need a Google Account to save a Colab Notebook
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 to function like 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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