Feature Stores: Your MLOps Competitive Advantage

Abstract: 

Features are properties that are used as inputs to a machine learning model. For instance, a recommendation application might use the total amount per purchase or product category as ones of its many features. Generating a new feature, or feature engineering, takes a tremendous amount of work. The same features must be used both for training, based on historical data, and for the model prediction based on the online or real-time data. This creates a significant additional engineering effort, and leads to model inaccuracy when the online and offline features do not match. Furthermore, monitoring solutions must be built to track features and results and send alerts of data or model drift. Feature stores offer a solution to this inefficiency. A feature store is a centralized and versioned catalog where teams can engineer and store features along with their metadata and statistics, share them and reuse them, and analyze their impact on existing models.
Some of the largest tech companies that deal extensively with AI, such as Uber, Twitter, Google, Netflix ,Facebook and Airbnb, have built their own feature stores in-house, indicating how important feature stores are to an efficient MLOps (machine learning operationalization) process. Given the growing number of AI projects and the complexities associated with bringing these projects to production, the industry needs a way to standardize and automate the core of feature engineering, and feature stores provide enterprises with a competitive edge as they enable them to expedite and simplify the path from lab to production.
In this presentation, we will cover what a feature store is, how it works and why it provides such an advantage in developing, deploying and monitoring AI in real business environments. We will show how to accelerate the development and deployment of AI applications with automated feature engineering, improved accuracy, feature sharing and glueless integration with training, serving and monitoring frameworks, using real customer examples.

Bio: 

Adi Hirschtein contributes 20 years of experience as an executive, product manager and entrepreneur building and driving innovation in technology companies. As the VP of Product at Iguazio, the data science platform built for production and real-time use cases, he leads the product roadmap and strategy.

His previous roles spanned technology companies such as Dell EMC, Zettapoint and InfraGate, in diverse positions including product management, business development, marketing, sales and execution, with a strong focus on machine learning, database and storage technology. When working with startups and corporates, Adi’s passion lies in taking a team’s ideas from their very first day, through a successful market penetration, all the way to an established business.

Adi holds a B.A. in Business Administration and Information Technology from the College of Management Academic Studies.

Open Data Science

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