Have you ever heard of feature stores? What do they mean in terms of machine learning?
The Feature Store in machine learning is where the features are stored and organized for the explicit purpose of being used to either train models (by Data Scientists) or make predictions (by applications that have a trained model).
A feature store could significantly ease your life if you frequently repeat the effort to code up feature transformations or copy and paste feature-engineering code from project to project.
This article explains What is feature Store in Machine Learning and lists its benefits and drawbacks. Let’s start!
Table of Contents
What Is Feature Store?
A tool for storing frequently used features is a feature store. It is possible to add new features to the feature store as they are created by data scientists for a machine learning model. This makes those features available for reuse.
When fresh illustrations, like users of an application, customers of a business, or items in a product catalog) are added, the previously developed features will be pre-computed so that the features are available for inference.
An extensive feature store
- uses data pipelines to convert raw data into feature values.
- s values for features and manages them.
- retrieves information for inference or training.
Features Of Machine Learning
As a first step, you should keep in mind that in machine learning, or MLOps, features (a single measurable property or characteristic of an observed phenomenon) are needed to feed models that make predictions. The information that the models will use is contained in those features. A row of an Excel spreadsheet or the pixels in a picture, for instance, could serve as that data.
Features are “any measurable input that can be used in a predictive model,” according to the dictionary.
Features serve as the “fuel for AI systems,” allowing ML models to be trained and predictions to be made. Predictions have the drawback of requiring a large amount of information or features. Prediction accuracy increases with data volume.
In order for the ML pipeline to use the features, the data for the features must be obtained from a data source and computed features must be stored after being computed (feature engineering, or turning the source data into features).
In general, machine learning relies on feature datasets that have already been created in order to properly train models. When we say datasets, we mean that the features are typically accessed as files in a file system.
Advantages And Disadvantages Of A Feature Store
- Feature reusability.
- Enhanced collaboration between team
- Time to value is shorter. Features are already computed for training or inference
- Complex logic centralization. Data scientists and ML engineers won’t need to worry about calculating complex feature values
- Monitoring potential. Feature stores can support health monitoring and drift detection to observe issues with features before they propagate to ML model predictions
- Possibly being rigid Organizations need a different feature store for each type of entity
- Complex integration. These can call for the blending of numerous technologies, including processing engines, streaming pipelines, and data warehouses.
- Restricts the degree of model customization. Different applications may benefit from different feature encodings that would be overlooked when all are using the same feature store
What Are Some Common Feature Store Tools?
- The feast is a feature management tool that is open-source. Feast only tracks features and retrieves them for training or inference; it does not compute features or stream new data. It merely manages data that is kept in other data sources like Amazon S3, Google BigQuery, and Google Cloud Storage (GCS). Alternatively, to using Kubernetes in AWS, Feast can run natively on Google Cloud Platform (GCP). Enhanced support for AWS is planned for upcoming releases. Because it is open-source, using it is free, but using the associated storage and transformation technologies may require more training.
- On top of Feast, Tecton is an enterprise-grade feature store. By adding features like feature storage and pipeline execution for transformation, it makes Feast easier to manage for businesses. A web UI is also provided for browsing and feature exploration. Tecton is more expensive than Feast because it is a managed solution, but it will be simpler for businesses to use and adapt.
- Another enterprise-grade feature store that can handle feature transformations, storage, and retrieval/serving is Hopsworks. It can operate on an incredibly broad range of infrastructure options, including AWS, Azure, GCP, Kubernetes, or even hardware that is kept on-site. Additionally, a wide range of data sources, including Snowflake, Redshift, and HDFS, are supported. Hopsworks has a web UI for browsing and exploring the features that are already available, just like Tecton. Hopsworks offers both supported (paid) and open-source (free) options.
When Should A Company Create Or Adopt A Feature Store?
When a company plans to create numerous models based on a single entity, such as customers, users, members, products, or items, feature stores are especially effective.
Reusing features across various models makes a lot of sense when the same type of example is used for numerous applications. In these situations, data scientists create features for a single model and then add them to the feature store for use by other models or analyses.