How the Feature Store of uniQin.ai will help other Data Science Projects

by Saikat Mazumder

There is great news for all Data Scientists and Machine Learning practitioners who are working with pricing techniques and solution building. Our company uniQin.ai is building a Feature Store for pricing analysis. This will immensely help different other projects who will work on similar subjects.

Let’s discuss what is a Feature Store in brief.

Every dataset that we analyze for getting the information, there are some features that need to be explored. The quality of these features that we build, decides the quality of prediction and interpretation of the data used by Machine Learning models. The process of creating new features is called Feature Engineering. Feature Engineering involves extracting and building important and influential features from the given dataset. The proper utilization of feature engineering increases the chance of better analytic outcomes from the data. 

A machine learning model trains from past records or past data and predicts the future outcome depending on its training from this past data. But, how good is the prediction? This entirely depends on the data or we can say the features the Machine Learning model has used during its training. Building new features from existing data can be a daunting task, often we have to use different composite techniques to achieve the most valuable features for the model. 

 What is a feature store?

A feature store is a place where we can commonly store the features. It provides readily available features already built for reusability to the different Machine Learning models. Feature store is not data storage but it transforms data and makes that as a feature that can be used for different algorithms. When a data scientist builds a new feature, this feature is added to the feature store, this feature can be accessed by other data scientists too for another project. 

Feature Store has the following attributes - 

• It transforms data and creates features using data transformation pipelines

• It manages the already build features and make them available for reusability 

• Some features are calculated in batch jobs and others calculated in the real-time system (e.g. real-time fraud detection system) 

There are typically two types of feature stores, Online, and Offline. They are as follows,

Online

Sometimes user-facing applications need to access and compute the features in very low latency. Application such as fraud detection techniques requires fast access to these features. In such cases, the features need to be accessed within seconds or sometimes in milliseconds. This is achieved by storing the data in a key-value database. This requires fast computing and quick retrieval of data.

Offline

This is for serving features in large batches and usually takes time to compute. The features are then used to train/test models. The offline stores are usually used for serving large amounts of feature data. The focus is on processing the high volume data from data storage like Apache Hive and BigQuery.

The feature store has many advantages in modern-day data science work. Some are listed below - 

1. Reduction of development time for reusability

It takes a really long time to create important features for a data by the data scientists in any organization. With the reusable features, data scientists can save lots of their time by not doing entire feature engineering and data transformation work, but they can focus entirely on model development. This reduces significant development time overall. The data scientist can simply made API calls to get the required feature for the model

2. Collaboration between different teams

It is easier to collaborate between the teams by using the existing features from feature stores. One team can use the feature developed in any other project.

3. Faster and smooth deployment

One of the main issues of deploying Machine Learning models into production is the feature mismatch. Often we train the model in development with some features that may change in production. This reduces the accuracy and consistency of the models. Feature store provides already built consistent data feature which has earlier been engineered and transformed. Training and deployment with the features from the feature store makes the model more consistent and easy to deploy in production.

Don’t need to rewrite complex feature engineering logic

Using feature stores reduces the time and effort to rewrite complex feature engineering logic each time we want to build a similar feature in different machine learning projects. 

Minor drawbacks of feature store

Integration can be complex sometimes due to the need of different things to integrate like data warehouse and data transformation pipelines etc. In some projects, where the features need to be customized as per need of the project, the features which are already served by the feature store may not be useful for the purpose. 

Conclusion

Today most of the AI and Analytics companies in the world are spending their valuable time making complex features for Machine Learning models. Leading companies like Google, Facebook, Twitter have their own feature store for AI operations. So, it is evident that the feature store from uniQin.ai is one step forward for a well built MLOps and Machine Learning pipelines for companies.

Loved the blog? Do check out another awesome blog by Saikat Mazumder on Dynamic Pricing And How It Helps Reshape Businesses.

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