Building out high-performing machine learning (ML) models tends to be a nightmare for most data scientists. It’s tedious work to access the right data. It takes time to ensure that all of that data is valid. And the time-constraints…
… Those painful time-constraints.
To make things a whole lot easier, ML Feature Stores were introduced.
They make all the difference in the world when it comes to smoother model deployment and ease-of-access to data.
What Is an ML Feature Store?
Okay, so you have a bunch of raw data that you could make useful.
Perfect. The very first step that your data scientists need to take is to wrangle and analyse that data.
Sounds easy, right? Not quite.
It requires specialised expertise, vast computational resources and plenty of time to get started.
So to take at least some of that burden off of data scientists, businesses use what’s called a feature store.
Basically, it’s a tool that helps them not only store their data, but also transform it into useful features that ML models can use directly for predictions or problem solving.
You see, raw data typically comes from a variety of data sources and of various different types. There’s structured, unstructured, batch, streaming, real-time and more.
The most important thing is that it all needs to get pulled and stored somewhere before it gets used.
That somewhere can be a feature store.
How Does it Work?
The job of the feature store is to then take that raw data and make it available for consumption.
In some cases, it provides feature pipelines that can also transform that data.
The pipelines produce and process features that can be used for both online and offline environments.
Those processed features can then be used by data scientists and ML engineers to produce high-quality predictive models, where the outputs then get used to solve a wide variety of business problems.
Feature stores also allow data scientists to work far more efficiently by allowing both the sharing and reuse of a feature. Which allows other teams to freely use them when needed.
Best of all, it results in a significant increase in productivity, since data scientists don’t have to work on setting everything up from scratch.
Feature stores also simplify some of the tedious work done by data scientists through providing an effective solution to common, but frustrating data engineering problems.
Simply put, a feature store functions as an effective interface between your data and your ML models.
To Summarise, an ML Feature Store:
- Makes raw data available for ML models to make use of.
- Aids in the production of high-quality predictive models.
- Features can be shared and reused by other teams when needed.
- Boosts productivity by saving time for data scientists.
Our ML teams help set the scene for smooth, high-performing ML models.
They’re experts in Machine Learning Engineering and if you just so happen to need a feature store, then you’ve come to the right place.
