Feature Stores: What are They & How Can They Benefit Your Machine Learning Initiatives?

Are you struggling to develop, deploy, and maintain machine learning models efficiently? Do your data scientists spend too much time on data preparation? Does it take over a year to get models into production?

If you answered yes to any of these questions, then a feature store could be a game-changer for your organization.

In this comprehensive guide, I‘ll walk you through what exactly a feature store is, benefits you can realize, when it‘s needed, and how to get started.

What is a Feature Store and How Does it Work?

Let‘s start with the basics – a feature store is a centralized repository designed to store, manage, and provide access to feature data used to train machine learning models.

It acts as a "source of truth" for features that can be reused across different models and shared between teams. The general workflow looks like:

  1. Data scientists and engineers extract and transform raw data into features
  2. These engineered features are stored and catalogued in the feature store
  3. Models can then query the feature store to retrieve current feature data for training or inference.

So in essence, the feature store sits between your raw data sources and ML models, serving up transformed feature data on demand.
Feature Store Workflow Diagram
Figure 1: Simplified diagram of a feature store workflow

This centralized access helps overcome many bottlenecks in model development.

The Challenges of Model Development

First, let‘s examine some of the key pain points in developing, deploying, and maintaining ML models:

  • Data prep is tedious and time consuming – By some estimates, up to 80% of a data scientist‘s time is spent simply finding, cleaning, and formatting data.
  • Model development takes too long – According to a survey by Algorithmia, it takes an average of over 12 months to deploy a model into production. See the distribution below:

Model Development Time
Figure 2: Time it takes to deploy ML models into production [Source: Algorithmia]

  • Inconsistent model performance – Many teams see significant gaps in model accuracy between training and inference caused by differences in the features used.

These challenges multiply as you build more complex models across larger, diverse datasets. Teams waste huge amounts of time re-engineering features and struggle to operationalize models.

This is where feature stores come in…

Benefits of Leveraging a Feature Store

Adopting a feature store can provide significant benefits including:

1. Faster Model Development with Feature Reuse

Cleaning raw data and feature engineering are very time intensive. With a centralized feature store, you can save features you engineer and reuse them for other models.

For instance, extracted data on average order prep times for a restaurant could be used across models predicting delivery times, kitchen workload forecasting, staff shift scheduling, and more. The reuse saves huge amounts of duplicate work compared to re-engineering features for every new model.

Some real world examples:

  • Uber saw 20-30% reductions in feature engineering time by reusing features [1].
  • Airbnb was able to cut time spent on feature pipelines by 50-90% with their feature store [2].

2. Improved Collaboration Between Teams

Allowing different teams to access the same curated features avoids duplicated work and siloed efforts. For example, your analytics team could build a churn prediction model using customer usage data engineered by the data science team.

At Uber, adopting a feature store improved collaboration across their 150+ person data organization [3].

3. Consistent Model Performance

Feature stores eliminate the problem of "training/serving skew" which happens when you use more features to train models but then only use a subset for inference.

By ensuring access to the full set of engineered features for both training and inference, you improve model performance consistency. One metric to track is AUC drift between training and inference – feature stores aim to minimize this.

4. Faster Automated ML Exploration

Feature stores integrate nicely with AutoML tools like H2O Driverless AI by providing access to clean, production-grade features to fuel the model training process.

At Comcast, their feature store helped cut AutoML experiment time by 50% [4]. Your data scientists can run more experiments faster.

5. Centralized Feature Monitoring & Governance

With all features in one place, you gain a centralized view into feature data including freshness, ownership, lineage, and more. This enables better monitoring of feature health and drift over time.

You can also apply centralized governance like access controls and compliance policies. This is critical as models move into production.

As you can see, leveraging a feature store provides multifaceted benefits ranging from accelerated development to improved governance. The more models and teams you have, the higher the potential upside.

Types of Features to Include in a Store

Now that you understand the benefits, let‘s look at what types of features you should include in your feature store:

  • Offline features – These are historical features retrieved from a database or data warehouse. They change slowly over time. Examples: customer lifetime value, average order size, seasonal sales patterns.
  • Online features – Features that need to be computed in real-time with very low latency. Examples: current promotional discounts, weather conditions, stock prices.
  • Categorical features – Features that take on one of a limited set of possible values like country, product type, account status, etc. These require encoding before use in models.
  • Numerical features – Features with continuous numeric values like a customer‘s age or an order total amount. Potentially requires scaling/normalization.
  • Engineered features – New features created from business logic, statistical measures, or even other ML models. Examples: sentiment scores, next product to buy predictions, etc.

The most effective models utilize a diverse mix of offline, online, categorical, and numerical features from across your business.

Real-World Examples of Feature Stores in Action

To make the benefits more concrete, here are a few examples of how leading technology companies leverage feature stores:

  • Uber built an internal feature store called Feast which helped them improve model accuracy while cutting development time. Features included trip distance, driver profiles, and customer ratings [1].
  • Airbnb created a feature store called Zipline which serves features to hundreds of internal models. Features included listing attributes, customer preferences, and local trend data [2].
  • Netflix uses their feature store Atlas to manage features related to movie recommendations including genre tags, actors, viewing history, etc [5].

The common thread is faster feature access, reuse, and sharing at scale across large model ecosystems.

When You Need a Feature Store vs Building Your Own

So when should you consider adopting a feature store? Here are a few signals:

  • You have data science and ML engineering teams building multiple models across different use cases
  • Significant time is spent on data discovery, cleaning, and feature engineering
  • Models suffer from accuracy gaps between training and production
  • There is redundant work happening across teams and models

Essentially, if your organization is struggling to industrialize model development, then a feature store is likely a must-have.

You have a few options on how to implement a feature store:

  • Build your own using open source tools like Feast or Hopsworks. Gives you full customization but requires significant engineering investment.
  • Leverage a managed cloud service like AWS Sagemaker Feature Store or GCP Featurestore. Quicker but less flexibility.
  • Use an integrated MLOps platform that includes a managed feature store like Comet, Allegro, or DataRobot. Combines all the pieces.

Unless you have very specialized needs, I would recommend looking at integrated MLOps platforms first. They provide a purpose-built feature store and end-to-end model management capabilities. You can compare the top MLOps platforms here to find one tailored to your tech stack and use cases.

Key Takeaways

To wrap up, here are the key points on feature stores:

  • Feature stores are centralized repositories to store, manage, and access curated feature data for ML models
  • Benefits include accelerated development, improved collaboration, and consistent model performance
  • Feature stores shine for organizations with multiple models and teams to coordinate
  • You can build your own feature store but integrated MLOps platforms provide faster time-to-value
  • Look for platforms that include automated feature engineering and monitoring capabilities

I hope this guide has provided you a comprehensive overview of feature stores and how they can benefit your machine learning initiatives. Please reach out if you have any other questions!

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