Introducing H2O Feature Store 1.0

Table of Contents

1. Introduction
2. The Motivation for a Feature Store
3. Key Concepts and Definitions
   • Feature
   • Feature Engineering
   • Feature Set
   • Project
   • Feature Store
   • Offline Store
   • Online Store
   • Feature Catalog
4. Capabilities of the Feature Store
   • Ingestion and Retrieval
   • Time Travel and Versioning
   • Security and Access Control
   • Search and Discovery
   • Derive Feature Sets
   • ML Datasets
5. Use Case: Fraud Detection at AT&T
6. Use Case: Churn Prediction at AT&T
7. Feature Store Demo
8. What's Ahead in Feature Store 1.1
9. Conclusion
10. FAQ

Introduction

Welcome to the webinar about the H2O Feature Store 1.0 release. In this webinar, we will explore the capabilities and benefits of the H2O Feature Store, discuss key concepts and definitions, and showcase real-world use cases. The H2O Feature Store is designed to streamline the feature engineering process, enhance model building, and improve the overall efficiency of data scientists and machine learning engineers.

The Motivation for a Feature Store

Data scientists and machine learning engineers spend a significant amount of time sourcing, cleaning, and transforming data before they can start modeling and gaining insights. This manual process often consumes 60 to 80% of their time, leaving little room for actual data analysis and delivering business value. The H2O Feature Store aims to address this challenge by providing a centralized repository for curated features that can be easily accessed and reused for different modeling projects. By enabling the integration of multiple projects, feature sets, and data assets, the H2O Feature Store reduces duplication, improves efficiency, and promotes collaboration.

Key Concepts and Definitions

Before we dive into the capabilities of the H2O Feature Store, let's define some key concepts:

Feature

A feature is a curated collection of data used for both model training and inference.

Feature Engineering

Feature engineering is the process of collecting, joining, encoding, and transforming data to Create features that are suitable for modeling.

Feature Set

A feature set is a collection of features that are assembled together for a specific modeling objective.

Project

A project is a repository of feature sets used to organize and separate work related to specific modeling or analytics tasks.

Feature Store

A feature store is a repository that manages and governs projects, feature sets, ingestions, and users. It provides a centralized location for storing and accessing curated features.

Offline Store

An offline store manages features for large-Scale storage and retrieval. It is optimized for batch processing and longer-term storage.

Online Store

An online store manages features for low-latency storage and retrieval. It uses caching to facilitate quick access to frequently used features.

Feature Catalog

A feature catalog is an inventory of the various feature sets discoverable by users. It provides information about the available feature sets and their corresponding capabilities.

Capabilities of the Feature Store

The H2O Feature Store offers several key capabilities that enable efficient feature engineering and model building:

Ingestion and Retrieval

The feature store supports the ingestion of data from various sources, including real-time and batch data. It provides APIs and interfaces to retrieve features for analysis and modeling.

Time Travel and Versioning

The feature store allows for time travel capabilities, enabling users to recreate the state of feature sets at different points in time. It also supports versioning, allowing users to track and manage feature set changes over time.

Security and Access Control

The feature store integrates with existing authentication and authorization systems, providing role-Based access control and ensuring data privacy and security. It allows users to manage permissions at the project and feature set level.

Search and Discovery

The feature store provides a user-friendly interface for searching and discovering feature sets. It allows users to filter features based on metadata and explore available features for their modeling tasks.

Derive Feature Sets

The feature store supports the creation of derived feature sets, which automatically propagate data from parent feature sets to child feature sets. This enables the efficient management and update of related features.

ML Datasets

The feature store allows users to create ML datasets, which are fixed data sets used for model building. ML datasets are constructed from feature sets and represent a specific state of the Universe for modeling purposes.

Use Case: Fraud Detection at AT&T

AT&T has successfully implemented the H2O Feature Store as part of their machine learning ecosystem. They have focused on accelerating the entire machine learning lifecycle and improving their fraud detection capabilities. By leveraging the feature store, AT&T has achieved significant results, including a cumulative model lift of 34% and a reduction in fraud events over a period of three years.

Use Case: Churn Prediction at AT&T

Another use case at AT&T involves churn prediction. By using the H2O Feature Store, AT&T was able to quickly identify Relevant features and improve the performance of their churn models. They experienced a significant lift in model performance within a short period of time, enabling them to deploy the models into production faster and effectively reduce customer churn.

Feature Store Demo

In the demo, we showcased the user interface of the H2O Feature Store and demonstrated how to Interact with feature sets. We explored features such as project creation, feature set registration, ingestion, retrieval, and the review process. The feature store provides a user-friendly interface for data scientists and analysts to easily access and work with curated features.

What's Ahead in Feature Store 1.1

The upcoming release of the H2O Feature Store, version 1.1, will introduce new capabilities and enhancements. This includes the ability to ingest and retrieve data directly from the user interface, providing more convenience and accessibility. The performance and scalability of the online feature store will also be improved to handle larger data requirements. H2O.ai will Continue to listen to user feedback and prioritize the development of new features and functionalities.

Conclusion

The H2O Feature Store offers a powerful solution for efficient feature engineering, model building, and collaboration in the field of data science and machine learning. With its time travel capabilities, versioning, security features, and ease of use, the feature store accelerates the entire machine learning lifecycle, reduces duplication, and improves productivity. Organizations like AT&T have experienced significant benefits and improvements in their AI initiatives by leveraging the power of the H2O Feature Store.

FAQ

Q: Can the H2O Feature Store be integrated with a private cloud or on-premises deployment?

A: Yes, the H2O Feature Store can be deployed in private clouds or on-premises environments. It is designed to be cloud-agnostic and can be easily integrated with different cloud technologies.

Q: Where should I start if I want to dip my toe into H2O AI systems as a software developer?

A: We recommend starting with the H2O documentation and exploring the recorded Talks on H2O's YouTube Channel. These resources provide comprehensive information and examples on using H2O AI systems. Additionally, joining the H2O community slack or Discord can be helpful for getting answers to specific questions and engaging with other users.

Q: How can I access the data in the feature store?

A: The feature store provides various APIs and interfaces for data ingestion and retrieval. Depending on your requirements, you can choose between downloading the data as files or accessing it via Spark for more advanced data manipulation and analysis.

Q: What are the future plans for the H2O Feature Store?

A: H2O.ai continuously works on improving the feature store and enhancing its capabilities. In the upcoming release, feature store 1.1, the focus will be on enabling ingestion and retrieval from the user interface, as well as further improving the performance and scalability of the online feature store. Rest assured that H2O.ai is committed to ongoing development and listens to user feedback to drive future enhancements.