Feature Store Architecture – Foundation of Machine Learning and its Benefits

Machine learning and Artificial Intelligence both have reached an inflection point. In 2020, businesses in diversified niches of varying sizes started to bring up their ML projects from research and experimentation to production on an industrial scale. During this, they also began to understand they were spending way too many resources and effort on feature definition and extraction; hence a need arose for robust feature store architecture.

With QWAK, let’s find out more about feature store and their benefits;

What Is a Feature Store?

A feature store is a vital component of the ML stack and any stout data infrastructure as it facilitates efficient feature management and engineering. A strong feature store architecture also enables simple re-use of features, including feature standardization across the company platform and feature consistency among the online and offline models. A scalable and centralized feature store enables organizations to drive ML processes at scale and innovate swiftly.

What are some business perspective benefits of robust feature store architecture?

Feature Store Benefits

From a business perspective, there are two major benefits of a feature store architecture;

1. With a comprehensive feature store architecture, you stand to gain better ROI from feature engineering via reducing cost per model, facilitating collaboration, re-use, and sharing of features.
2. A quicker time is needed to market for the new models, compelled through boosted productivity of ML engineers. Enabling organizations to decouple storage implementation along with features serving API from ML engineers. Doing so essentially frees them to work with models and not on the latency problems, resulting in efficient online serving.

Better Understanding of Features

As now we have talked about what a feature store is and how a feature store architecture is beneficial in multiple niches, it is time to focus on what features are;

Essentially you can contemplate a feature as a data type inside the data set.

You have both online and offline features, which are equally important. However, these have to do with how the information changes over time. Every machine learning project has a variable that has insights. What you need for your machine learning models to work are features; without these features, your machine learning model cannot turn into a functioning entity that delivers value for your organization.

Due to this fact, you must understand how your model uses any feature. Plus, one also needs to understand how the dynamic online features differ from the static offline ones. Once you understand how the features work, it will make building a feature store architecture easier for your business.

Why do you need a Feature Store?

Having a good feature store architecture is essential as it helps create a workflow for your business – that is workable. A feature store helps with already assisting MLOps processes. Letting your organization create sound and excellent pipelines makes a data scientist’s job easier. How? An ML engineer also uses a feature store; this allows the machine learning engineers to pick the feature they need without consulting with the data scientists.

All of this makes the building process of the machine learning model streamlined and fast. It also enables your business to create more models in the future, giving you the possibility of advancing to the next level. That being said, you might find it difficult to choose between standalone and integrated solutions – it all, however, depends on your particular needs.

Feature Store Architecture – a Foundation for Machine Learning

As organizations leap to build production level ML models, numerous lessons are being learned and coming to light regarding the supporting infrastructure. Maintaining an efficient and centralized feature store for several important types of use cases is crucial for getting faster delivery and higher ROI.

Concepts of a Feature Store

Standard feature store has a few key concepts which revolve around it; the concepts are:

Online Feature Store

The online application needs a feature vector sent to the ML model for predictions.

ML Specific Metadata

Facilitates re-usability and discovery of features.

ML Specific API and SDK

High-level operations for online access and training feature sets.

Materialized Versioned Datasets

Keeps and maintains feature set versions to train ML models effectively.

What Future Holds for Feature Stores

It resonates still quite early in the game for feature stores; businesses that are going beyond experimenting by actively moving ML projects to production now understand the need to have a centralized repository to retrieve, store, update and share features.

• An existing data infrastructure shall and must cover at least 90% of the feature store requirements, including consistency, streaming ingestion, consistency, versioning, and data catalog for getting the desired outcomes.
• Creating a lightweight feature store API and integrating it with your existing storage in-house solutions is sensible.
• An organization should join forces with cloud vendors and the community to achieve compatibility with standards and state-of-the-art ecosystems.
• Organizations should always be prepared to migrate to a properly managed service or an open-source option as the market develops.

Conclusion

Feature stores hold massive importance as they enable data scientists to re-use features rather than building them time and again for various models – essentially saving them time and effort. A robust feature store architecture better automates the feature engineering process, which is a crucial part of the MLOps concept.

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