The features stores are the next big thing and the future of the Modern data stack.
Let’s look into some common features of the feature stores.
Structures of the Feature Stores
Recently feature stores are in a new traction – the commercial offerings.
Be it a standalone project, or as an extra addition for the pre-existing platforms, or even a part of the working platform; feature stores are everywhere.
Now there is much variety in a feature store, also these come in multiple sizes and shapes. For this post, we will elaborate on the essential features of feature stores.
Tracking is the essential aspect of any feature store, it keeps track of which of the transformations happened to which of the data sources.
Feature Store a Fully Modern Data Stack – Common Features
It can be the regular aggregations and joins or even window operations. Also advanced capabilities can also be obtainable with automatic feature engineering like feature encoding, feature synthesis, or data extraction.
Feature stores does not need to:
· Actually carry out these operations
· Store the resultant data
Additional fragments of the feature stores might be answerable for these jobs.
Offline and Online Mode
Besides management of the features, primary task of feature stores is serving these features to the models. It can happen in either real-time or offline.
There lies an vital difference among the two cases;
Feature Store Offline Mode
The offline mode needs to generate a big dataset of records across the different features, offering it to model for prediction/training.
Quickness at which this is done is not persistent, but it must handle the larger datasets.
Feature Store Online Mode
Online mode for feature stores generates feature for record data – making predictions.
Here speed is important, and in some use cases, this whole process requires to be completed under only a second.
The use cases above need different architecture, however, the feature stores need to abstract the intricacy and offer serving layers.
With a proper design – feature stores can let the data professionals stop problems in columns and tables however in actual commercial objects such as products and customers.
This effectively turns customer churning into getting and combining tables such as customer;
· Product info
· Product usage, and more
-into one linking to product and customer entities. Feature stores know all relevant and important data for business objects/entities in any organization and can connect correctly.
With growing applications and features of a feature store, one finds themselves asking “which of the models will be altered while we change this feature or update it?”
Feature stores such data in feature hierarchy. Allowing users easy visualizations of how feature entities link, plus the tables, columns, and transformation involved with the related models.
Feature reuse and discoverability are essential selling points of feature stores, and the feature lineage becomes highly important to let users understand how the features are utilized with their relationships.
It is important to acknowledge and remember that, like the life like problems, ML problems can often involve varying and multiple data sources while also having time factor attached to them.
Time reasoning can turn complex and give even a tenured data scientist a run for his money. The risks of mishandling time-based data in a ML problems can become crucial, as one can drip data from future and nullify the results completely.
Feature Model Encapsulation
Feature stores offer means for serving predictions for offline and online use cases.
Today it is not quite uncommon for ML platforms to deploy a ML model by way of an API endpoint. Doing so, mostly demos well, but in production, application developer won’t appreciate the need tracking the needed features.
To add to the annoyance, anytime a model gets updated with new features; the application will need an update too for accommodating newer API.
Feature Stores as Future
There are many different forms that feature stores take today.
Continuing the advancements, we are offering tools, repositories, and knowledge-based materials that will help you build on the cloud data warehouses directly while additionally enabling a full declarative flow simplifying operational artificial intelligence.
By inputting the data first then taking in account machine learning workflow, we help enable data teams for the deployment of always evolving analytical models. That too, from the client churn to portfolio forecast in days or in hours but never taking weeks or months.
Our tools and platform are the best and true potential feature store – when completely integrated to a declarative AI workflow. Get in touch with Qwak for more information and research-backed consultations.