Why are data scientists using Feature Stores?
When we look at the data science field, we see many different technologies are gaining momentum because they are making data modelling easier, more flexible and more accessible. Feature store is one of those technologies and becoming the need of data scientists. This technology is used in the field to maintain the flow of data between database and model. Since it is very helpful in improving the way and performance of modelling we should be aware of it. In this blog post, we are going to talk about the feature store using the following points.
Table of content
- What is a Feature?
- What is a Feature Store?
- Why Feature Store is beneficial for Data Scientists?
- Why UnifyAI’s Feature Store?
What is a Feature?
The reader should know that machine learning models work based on past information. In most examples, we find that data is in tabular form where rows are data points, and columns are attributes describing those data points. A feature is an attribute that we use to describe each example.
In a simple modelling procedure, we use mathematical algorithms that help make predictions based on the old examples for the new examples. This process is called inference. The old example generally refers to training data and feature engineering is a process where the modeller applies transformation and selection to raw data so that suitable features can be consumed by the model.
In the above example of data, we can see that there are four features and one target variable in the iris data.
What is a Feature Store?
A feature store can be thought of as a tool storing features that are useful for the ML model, and when it comes to training or prediction time, it serves the old or new data to the model.
After looking at the above points we can infer that A feature store is a start point of the modelling procedure and end point of data flow because this is where a data scientist easily access and discovers data to train, evaluate and execute machine learning models.
It takes place in the system because it helps in keeping track of the lifecycle of the data which the model is using. We can consider it as a junction where we group the features created in the multiple data sources. Purposely feature stores are being utilised to ensure data correctness, maintain the data flow, and reuse the features.
In case of adding new examples, the feature facilitates previously developed features pre-computed so that the availability of features for inference can be maintained.
The above flow chart explains the place of feature stores in the modelling procedure.
Why feature store?
Why Feature Store is beneficial for Data Scientists?
Feature store is not an old technology that comes in front of us. Instead, the first public feature store was applied by Uber in 2017 named Michelangelo Palette. Feature store helps in solving some of the major data modelling problems:
- Exclude higher complexities during development
In the above points, it is discussed that the modelling procedure consumes data in two places, one in training and the other in inference. Talking about the training time, we find that data consumption happens in batches and old databases and BigData options provide the facility of serving data in batches.
When it comes to inference time, it is suggested not to rely on a batch prediction strategy. If the feature store and batch prediction strategy are not there, then data scientists require to set up different solutions for each new project.
- Exclude complexities in debugging models in production
A good feature store applied in modelling provides a facility to retrain and debug the model when in production it is not performing as expected. This facility is called point-in-time correction and becomes very helpful in retraining and checking the model with the same data and new data.
- Cost reduction in feature reusability
When applying ML models in an organisation, it is found that there are always more than one use-cases that need to be resolved using the same feature from the data. If feature store is not applied, reusing the similar feature for a different use case requires a new set-up and costly storage. Feature stores provide flexibility of reusing the feature for different use cases.
- Reduces the effect of feature drift
This one is the most important point in favour of the feature store. When new examples come into the databases, the data distribution changes and this causes the degradation of the model, which is not trained with a new example. Feature store provides the facility of retraining the model on new data. This feature of the feature store helps in maintaining the model performance.
Why UnifyAI’s Feature Store?
We at DSW are democratising the power of AI using our flagship platform UnifyAI. This platform uses some essential components to build, orchestrate and leverage AI capabilities for use cases across the domains, and the feature store is one of those components. Using feature store, this platform helps reduce the time of building and resolving new use cases. Understanding and working with feature stores is easy but understanding its placing in an end-to-end development procedure is complex. A feature store always requires a place from which it can take part in model building as well as in model orchestration. Using the full capability of feature stores, UnifyAI ensures we can resolve use-cases as much as possible using common features. This reusability of features from the feature store helps us reduce time into processes like data validation, cleaning and transformations. This time reduction helps UnifyAI focus more on model accuracy and performance. Other components connected to feature stores are:
- Data Pipeline: gives features to feature stores
- MLOps Pipeline: Extracts features from feature stores.
- Orchestrator: Fetch required features
The below diagram tells the basic story of UnifyAI’s feature store.
Also, In the above sections, we have seen how feature store plays an important role in the data science process. We understand and keep ourselves up-to-date with such useful and new technologies and take responsibility for utilising them with real-world problems wit the aim of making AI work for everyone.
Final Words
As many organisations from every domain are thinking of applying AI and ML to resolve their use cases, it becomes essential to understand how they can utilise their data fruitfully. Moreover, as technology is gaining momentum, it becomes compulsory to understand the trending topics in the technology field and topics that are beneficial in opting. Feature store is one of those techniques which not only improves the quality of AI-enabled decisions but also scales the capacity of taking AI-enabled decisions.
About DSW
Data Science Wizards (DSW) is an Artificial Intelligence and Data Science start-up that primarily offers platforms, solutions, and services for making use of data as a strategy through AI and data analytics solutions and consulting services to help enterprises in data-driven decisions.
DSW’s flagship platform UnifyAI is an end-to-end AI-enabled platform for enterprise customers to build, deploy, manage, and publish their AI models. UnifyAI helps you to build your business use case by leveraging AI capabilities and improving analytics outcomes.
Connect us at contact@datasciencewizards.ai and visit us at www.datasciencewizards.ai
