Despite machine learning algorithms being an excellent way to improve applications, companies are still struggling to put things into production. MLOps has grown in recent years, enabling companies to do a good job, but there is still a long way to go. Putting a model into production is still the most difficult struggle in data science.
Data innovations like feature stores are becoming a vital piece of the puzzle to enable your data science projects to scale to the next level. As the name says, a feature store is a database of features that allow you to rapidly work with data instead of creating it yourself all the time. It makes it possible to create accurate models more quickly without having to rely on features being created manually.
Feature stores have traditionally been the purview of massive corporations like Google, Netflix, and Facebook. These companies have the resources and manpower to create a large catalog of feature stores. However, the use of MLOps is making the process easier for everyone in this field. Eventually, we might see feature stores be a ubiquitous part of the model engineering process.
What Are Feature Stores?
Features are what make your machine learning model more accurate. You can think of them as the variables you manipulate to help your algorithm learn and make predictions. They are specific to data, meaning you have to calculate them quite often. However, you can also create a database of these features to enable you to calculate and manipulate data more quickly.
It enables you to create an accurate model without all the waiting that comes from having to calculate these features manually all the time. If you have to create features online, you need the plaintiff processing power and fast memory-based database systems. Because of that, feature stores are typically out of reach for most of the machine learning practitioners in the industry.
Reasons to Have a Feature Store
A feature store makes it easy for you to develop your model quickly. On top of that, your model is going to be a lot more accurate, meaning you can get real tangible results from your machine learning initiatives. The other great benefit of these feature stores is the fact that you will be able to transition to production much more easily.
Since you have feature stores available, it is all about taking that and putting it into production. Operationalizing your machine learning becomes a snap, and you don’t have the problems that other people in data science would.
Feature Stores in the MLOps World
The good news about feature stores is that MLOps is making it a lot easier to produce them. This benefit enables companies in data science to save time and effort instead of rebuilding features for different models. Automating how you work with a feature is one of the biggest innovations that MLOps brings to the table. A growing number of projects in the machine learning and data science industry are completely transforming the way we look at how feature stores and model development work.