With the way machine learning unfolds, data used in testing and modeling must come at the best quality relevant to the learning process. From data cleaning to data labeling, the process can be tasking. It’s not only about creating a simple data pipeline, machine learning and data processing extend beyond that. Machine learning often isn’t a one-and-done deal. It’s used more and more for operational purposes and for creating data-driven applications. That’s why data teams turn towards creating or using a feature store. But first, what is a feature in machine learning?
Basics of features in ML
Data comes in raw formats. To use it in any data project, machine learning projects specifically, it needs to be transformed into features. Features are, simply put, inputs used in machine learning to perform predictions.
Features in ML are independent variables or measurable pieces of information that classify the type of data used in machine learning. They are building blocks of making predictions, and for example, in some cases, they can be defined as age, gender, weight, and so on, depending on the data set and what it describes. Defining features is called feature engineering and it’s often a time-consuming process.
There are two types of features: continuous and categorical.
Continuous features
These are numerical values, within a certain range, that represent detailed information on selected data. They are more precise, or accurate since they are defined by numbers. For example, weight, height, etc.
Categorical features
These are categorical values or information according to which data is divided. Basically, categorical data represents a specific category or class. For example, gender, other fixed data, etc.
What about feature engineering?
Feature engineering is the process of raw data extraction and transformation, and turning it into features used in ML. Simply, it’s the action of converting raw information into usable and useful features that describe those observations. It leverages data specifically for machine learning by creating variables that can provide more information and provide accuracy in ML training and models.
The rise of feature store
As mentioned above, feature engineering is time-consuming and not an easy task. The success and accuracy of machine learning predictions and insights depend on the quality of feature engineering. This is where feature stores come into play.
A feature store is an ML-specific data system designed to store commonly used features. A feature store transforms data, stores and manages feature values, and retrieves data for training and inference. For machine learning predictions to work, models have to go through a training process based on historical data and prepared features. Once the model has been trained, new data inputs need to be, sort of, operationalized through pipelines that will transform new data in line with defined features from the training process.
Image 1: Feature store system
Components of a feature store
Some standard components of a feature store are transformation, storage, serving, monitoring, and feature catalog.
Transformation refers to data transforming from raw format into neat features. Feature stores need to perform transformations by defined definitions and rules. Storage is keeping features stored for future reuse. It’s a centralized depository for features that can be used across multiple different models. It consists of two databases, online and offline. Serving is oriented towards exactly that – serving features to models.
Operational monitoring does what it says, it monitors operations inside a feature store.
A feature catalog or feature registry is a repository of features based on definitions and metadata that anyone can easily find and use.
Image 2: Feature store components
How does it influence machine learning?
Feature stores are used as centralized storage where commonly used features can be accessed and processed across multiple ML models, teams, and entities. But they are not only used as “storage”. They transform raw data from various sources into features used for training ML models. By minimizing the data engineering part used in data transformation, the feature store provides quicker training-to-model time and minimizes the effort levels in data preparation.
For big companies that are dependent on machine learning to provide services or products, feature stores are a great way to handle large amounts of data processing for data training and modeling.
Benefits of feature store
Not always will you need a feature store. You will use it only if you have many models that share the same features so you don’t have to go through the feature engineering process multiple times. So, for such companies feature stores bring a lot of benefits.
Feature reuse
When features are developed, they can be stored for future use. This allows them to be shared among teams for any future project, which improves the speed of model delivery.
Centralized data and features storage
Feature store keeps features in one place which implies centralization. It’s a centralized platform for the development, storage, modification, and reuse of ML features.
High model performance
Feature stores have centralized feature pipelines with the same feature definition, so their implementation remains the same through training and inference to modeling. This speeds up the process from raw data processing to have data ready for machine learning since it abides by the same rules and definitions.
Security and data governance
Knowing which data was used in machine learning, benefits the data team when they have to go through iterations or solve issues. Feature store also solves the issues of sensitive data. If it’s not needed in the modeling, it can be cleaned and removed before it was put in the feature store. That way data scientists don’t have to worry about weeding out sensitive data from training.
Enhanced collaboration and feature sharing
Having a centralized feature store allows different teams to access data for their ML projects without interference. Various projects might require the usage of the same features, so such stores diminished the need for them to go through multiple levels of authorization to access data. Data teams can also share ideas, improve the speed of feature engineering, and solve possible issues.
When do you need a feature store?
If you’re a company that bases its product or services on machine learning and complex models, you’ll definitely benefit from a feature store. We can say that it almost becomes necessary. If your machine learning invites complexity, feature stores could eliminate complications and high costs of handling features and align model testing and serving.
Especially if you use operational ML, then a feature store is a must since your users count on you to deliver accurate and on-time information or insights.
Whether you use a feature store or not, it’s a sort of, personal choice. You know your requirements and teams best, so you’ll have to weigh out the benefits versus cons of using feature stores. But if you decide to do so, it’s probably a better option to use existing providers than develop your own.
