Introduction

In the realm of artificial intelligence (AI), the significance of a well-structured and efficient feature store cannot be overstated. A feature store serves as the central repository for all the features used in machine learning models, ensuring their availability, consistency, and reusability. However, obtaining approval for investments in such infrastructure can be challenging due to the inherent uncertainty surrounding AI projects: the investment boards in large organizations are balancing the costs of such an investment against a set of uncertain returns, and that makes this evaluation all the more complex.

To overcome this challenge, it is essential to develop compelling value cases that quantify the benefits and costs associated with implementing a feature store. By providing specific, measurable data, organizations can reduce uncertainty and make informed decisions regarding infrastructure investments. This is a proven tactic, based on solid scientific evidence. 

The Need for Quantifiable Value Cases

Prospect theory, a well-established psychological theory, teaches us that in conditions of uncertainty, people tend to overestimate costs and underestimate benefits. The late Nobel-prize winning author Daniel Kahneman talks about the asymmetrical value function for costs and benefits: we just don’t seem to weigh both factors the same way in conditions of uncertainty - and we are therefore biassed in our judgements of AI investments.

This bias can hinder the adoption of new AI technologies and infrastructure, such as feature stores. To counteract this bias, it is crucial to reduce the uncertainty by providing quantifiable value cases that clearly demonstrate the tangible benefits of implementing a feature store.

Evaluating the Benefits and Costs of a Feature Store

To develop a comprehensive value case, the following key factors need to be evaluated:

Benefits of Implementing a Feature Store:

• Cost Saving via Efficient Model Execution: By enabling the reuse of precomputed features, a feature store reduces the computational resources needed for model training and inference down the line. This results in lower (cloud) compute costs, especially for models that require intensive processing.
• Improved Update Efficiency: A feature store centralizes feature computation and storage. This eliminates the need for individual data pipelines to recompute features for each model update. As a result, model updates can be performed more quickly and cost-effectively.
• Accelerated Model Development: With a feature store, data scientists and ML engineers can easily access and combine existing features for new (versions of existing) models. This reduces the time and effort required to gather and prepare features, accelerating the model development process.
• Increased Model Quality: By ensuring consistency and quality of features across different models, a feature store helps in producing more reliable and accurate machine learning (ML) models. This can lead to improved business outcomes and cost savings through better decision-making.
• Reduced Time to Market: With faster feature engineering lifecycles and the following acceleration of model development, a feature store enables quicker deployment of (new versions of) ML models into production. This accelerated time to market can provide organizations with a competitive advantage and lead to earlier realization of ROI.
• Improved Governance and Risk Reduction: A feature store provides a central repository for feature definitions, metadata, and usage history. This facilitates better governance and risk management practices, reducing the likelihood of errors or inconsistencies that could impact model performance and business decisions.
• Reduced Ramp-up Time for ML Teams: By providing a standardised, modular and centralised platform for feature management, a feature store reduces the onboarding and learning curve for new ML engineers. This can result in faster and more productive team ramp-up, saving time and resources.

For each of these factors, we can establish a set of calculation metrics that will be centred around 

• The number of models that are developed and/or run in production
• The number of days of a data scientist or ML engineer saved for each of these models

Costs of Implementing a Feature Store

Here, we will distinguish between two different implementation strategies.

Building a feature store in-house:

The costs of building a feature store in-house can be described as a function of the following three factors:

Initial Development Cost (Year 1): This cost includes the expenses incurred during the first year of development, such as:

• Salaries and benefits for software engineers, data engineers, and infrastructure engineers
• Software and hardware costs, including servers, storage, and networking equipment
• Development tools and licenses

Operating Cost per Year (from Year 2 onwards): These are the ongoing costs incurred each year after the initial development phase. They include:

• Salaries and benefits for operations and maintenance personnel
• Software and hardware maintenance and support costs
• Cloud computing costs for hosting the feature store and related infrastructure
• Costs associated with compliance and security measures

Maintenance Development Cost per Year (from Year 2 onwards): These costs are related to ongoing development and enhancements of the feature store. They include:

• Salaries and benefits for software engineers and data engineers
• Costs associated with new features, bug fixes, and performance improvements
• Costs associated with integrating the feature store with other systems and technologies

The alternative strategy to building a feature store yourself, is to buy one from a vendor.

Buying a Feature Store from a Vendor:

The costs of implementing a feature store purchased from a vendor can be described as a function of the following three factors:

Total Purchasing Cost:

• Licensing costs: Commercial feature store solutions typically require licensing fees, which can vary based on the number of features, users, or usage.
• Integration costs: Integrating a vendor-provided feature store with existing systems and data pipelines may require additional costs for customization and professional services.

Operating Cost per Year:

• Cloud computing costs: The cost of hosting the feature store and related infrastructure on the vendor's cloud platform.
• Support and maintenance costs: Fees associated with ongoing support and maintenance provided by the vendor.

Maintenance Development Cost per Year:

• Customization costs: Costs associated with customizing the feature store to meet specific organizational requirements.
• Upgrade costs: Costs associated with upgrading to newer versions of the feature store software.

All of the evaluation criteria above can be made very specific and quantifiable - and this is exactly what we need in order to come to a more objective, unbiased evaluation of these investments. To make it even easier, Hopsworks is providing you with an example calculator that can accelerate and facilitate your own thinking on this topic.

Utilizing the Hopsworks Calculator

Hopsworks provides a draft spreadsheet calculator that allows customers to estimate the costs and benefits of implementing a feature store. The calculator considers various factors such as the number of features, the number of models, and the expected improvement in model performance. While the calculator does not need to be 100% accurate, it provides a valuable starting point for organizations to assess the potential ROI of a feature store implementation.

Summary

In this blog post we provided AI and ML enthusiasts with a useful framework for developing compelling value cases for feature store implementations. By quantifying the benefits and costs associated with a feature store, organizations can overcome the uncertainty surrounding AI projects and make informed, unbiased decisions regarding the required infrastructure investments.