Optimize Feature Engineering with H2O Feature Store

Optimize Feature Engineering with H2O Feature Store

Table of Contents:

I. Introduction II. Motivations for a Feature Store III. Concepts and Definitions A. Features B. Feature Engineering C. Feature Set D. Project E. Feature Store F. Offline Store G. Online Store H. Feature Catalog IV. Capabilities of the Feature Store A. Ingestion and Retrieval B. Time Travel C. Versioning D. Security and Access Control E. CLI and UI V. Use Case: Fraud Detection at AT&T VI. Use Case: Churn Model Improvement at AT&T VII. Demo of the H2O Feature Store VIII. Future Developments IX. Conclusion X. FAQ

Article:

Introduction

The H2O Feature Store 1.0 release is a powerful tool for data scientists and machine learning engineers. It provides a scalable framework for managing and governing projects, feature sets, ingestions, and users. The feature store is designed to optimize the feature engineering and model building processes, connect information across multiple platforms, and establish consistency and transparency across the machine learning life cycle. In this article, we will explore the motivations for a feature store, the concepts and definitions involved, the capabilities of the feature store, and two use cases from AT&T. We will also provide a demo of the H2O feature store and discuss future developments.

Motivations for a Feature Store

Data scientists and machine learning engineers spend a significant amount of time sourcing data, cleaning it, and performing all sorts of curation so that they can do the other things. The H2O Feature Store is designed to provide a utility in an economy of Scale where over time, multiple projects, additional feature sets, and data assets get integrated. This starts shrinking so that there's more time to do things that are of greater value to the organization once You have the right data pipelines in place. The feature store provides a scalable framework to facilitate that.

Concepts and Definitions

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

B. Feature Engineering Feature engineering is a process that collects, joins, encodes, and transforms data for consumption.

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

D. Project A project is a repository of feature sets used to organize and separate work.

E. Feature Store A feature store is a repository that manages and governs projects, feature sets, ingestions, and users.

F. Offline Store An offline store manages features for large-scale storage and retrieval.

G. Online Store An online store manages features for low-latency storage and retrieval.

H. Feature Catalog A feature catalog is an inventory of the various feature sets discoverable by users.

Capabilities of the Feature Store

A. Ingestion and Retrieval The feature store supports ingestion and retrieval of data from a variety of data sources, including Snowflake, Databricks, AWS, Azure, and Google Cloud. It also supports time travel, which allows you to recreate the state of the feature sets at a different point in time.

B. Time Travel Time travel allows you to recreate the state of the feature sets at a different point in time.

C. Versioning The feature store supports major and minor feature versions, which cover incremental ingestions and schema changes, respectively.

D. Security and Access Control The feature store provides role-based access control and authentication and authorization. It also supports a review process, which allows you to create a stop gap in the feature publishing pipeline.

E. CLI and UI The feature store provides a command-line interface (CLI) and a web user interface (UI) that allow you to manage and govern projects, feature sets, ingestions, and users.

Use Case: Fraud Detection at AT&T

AT&T uses the H2O Feature Store to combat fraud, which is a multi-billion dollar industry. The feature store provides a scalable framework to streamline data quality management across the machine learning pipelines. It helps to improve accuracy by having well-curated features and standardizing and reusing data pipelines for the creation of feature sets and the registration of the offline and online store. The feature store also helps to protect customer privacy and compliance workflows.

Use Case: Churn Model Improvement at AT&T

AT&T uses the H2O Feature Store to improve its churn model. The feature store provides a variety of features that are curated across data science communities and promotes the usability of data from the feature store. It also provides metadata that is critical for understanding how the data is being built. The feature store helps to accelerate the entire machine learning life cycle and promotes the use of citizen data scientists.

Demo of the H2O Feature Store

The H2O Feature Store provides a web UI that allows you to manage and govern projects, feature sets, ingestions, and users. You can also use the CLI to manage and govern these components. The feature store supports ingestion and retrieval of data from a variety of data sources, including Snowflake, Databricks, AWS, Azure, and Google Cloud. It also supports time travel, which allows you to recreate the state of the feature sets at a different point in time. The feature store provides role-Based access control and authentication and authorization. It also supports a review process, which allows you to Create a stop gap in the feature publishing pipeline.

Future Developments

The H2O Feature Store 1.1 release will include the missing UI capabilities for ingestion and retrieval. The feature store will Continue to improve the performance of online stores to handle the huge data requirements that are being asked to support.

Conclusion

The H2O Feature Store 1.0 release is a powerful tool for data scientists and machine learning engineers. It provides a scalable framework for managing and governing projects, feature sets, ingestions, and users. The feature store is designed to optimize the feature engineering and model building processes, connect information across multiple platforms, and establish consistency and transparency across the machine learning life cycle. The feature store provides a variety of features that are curated across data science communities and promotes the usability of data from the feature store. It also provides metadata that is critical for understanding how the data is being built. The feature store helps to accelerate the entire machine learning life cycle and promotes the use of citizen data scientists.

FAQ

Q: What is a feature store? A: A feature store is a repository that manages and governs projects, feature sets, ingestions, and users. It provides a scalable framework to optimize the feature engineering and model building processes, connect information across multiple platforms, and establish consistency and transparency across the machine learning life cycle.

Q: What are the concepts and definitions involved in a feature store? A: The concepts and definitions involved in a feature store include features, feature engineering, feature sets, projects, feature store, offline store, online store, and feature catalog.

Q: What are the capabilities of the feature store? A: The capabilities of the feature store include ingestion and retrieval, time travel, versioning, security and access control, and CLI and UI.

Q: What are some use cases for the feature store? A: Some use cases for the feature store include fraud detection and churn model improvement.

Q: What are some future developments for the feature store? A: The H2O Feature Store 1.1 release will include the missing UI capabilities for ingestion and retrieval. The feature store will continue to improve the performance of online stores to handle the huge data requirements that are being asked to support.