The biggest challenge when building models from a machine learning algorithm is being able to have an effective feature store. Feature engineering is critical because it is one of the most complicated pieces of bringing a model into production. If you know how to do it right, a feature store makes the process of tuning and improving your model easier. However, every project needs its own feature engineering efforts, so you need to have access to a platform that allows you to do feature stores and feature engineering on a massive scale.
On top of that, you need that platform when it comes to various feature engineering techniques that can happen. Feature engineering machine learning projects are not that common because each industry and platform will have its own way of dealing with things. However, you can build a feature engineering framework that allows you to train models quickly and scale to wherever you need to be.
The main reason that people don’t get things right is they don’t have a feature store with good architecture. The best feature engineering techniques work in the same way as DevOps does for software engineering. A feature store is great if you have a registry of features, and then you can do feature engineering techniques on top of that. The architecture works by importing raw data to extract features and put them into the registry.
The good thing about doing things this way is the registry can then scale effectively. You can think of it as a massive database for your features, and you can then extract them as needed. The dataset is also tied to these features, meaning that everything works in one simple package. Your machine learning algorithm can then be applied to build a model that works for your needs.
Another thing you can do in feature engineering is to start with machine learning modules that can easily be shared among other teams. This means your feature store would essentially be generic, making it possible for you to bring your machine learning algorithm to life. It is one of the many reasons a feature store would become a scalable project.
This feature engineering framework is quite effective, and it is an excellent way for you to scale things up. When you can share features, you can focus on doing the right things with your machine learning algorithms. You also have the benefit of features stores with your team as well. Everyone in your organization benefits, and you can move on to something better.
The final piece of the puzzle when building a feature engineering framework is governance. Just like other types of data, a feature store will need to be governed well on top of that. You want to make sure that your feature stores are backed up, and the feature engineering job is well done to the liking of everyone in your organization and from potential legal repercussions. You also want to enable your machine learning algorithms to be reproducible by everyone else. To that end, you need effective MLOps to do a good job. All of this comes together to make feature engineering easy and scalable.