Feature Engineering Can Help Your Machine Learning Pipeline

Many machine learning applications are static because companies don’t have the skillset to build models that offer personalization. Programs with personalization have a much more complicated machine learning pipeline. You need an excellent feature store, and you have to also integrate real time feature engineering. This process can get complicated, and that is why many companies don’t bother with the trouble.

No matter what companies do, more than 85% of ML projects never make it into production. Because of that, understanding and building ML pipelines become a lot more difficult. Even if you get past the first hurdle when building your ML project, you still need to actually make it work in a functioning environment. ML pipelines offer many businesses and technological challenges. Because of that, you also need clever engineering to make certain features work.

ML Pipelines in Your Organization

Ultimately, the reality is that building machine learning projects is difficult at its core. You need to ingest, clean, and turn data into a working model. However, the machine learning pipeline is complicated, and it is made even worse by the fact that you need to iterate continuously to ensure that you have the best model at all times. This is the major stumbling block for companies, as they don’t have the opportunity to iterate and deliver with the ML pipelines they can use.

The biggest thing that a feature store does the year machine learning pipeline is that it makes it easy for you to optimize your model for a variety of use cases. This makes personalization possible, making the user experience a lot better.

Building Your Feature Store

Once you have figured out the need to have a good feature store, the next step of your machine learning pipeline is the feature store. A feature store offers a great advantage for companies when making projects easy to start and deliver on time.

The steps are pretty much the same as normal, as your data scientists will need to start with data ingestion and cleaning. After the cleaning process, you go through the model training process as well. Once that has been finished, you can now deploy and do the typical monitoring steps in most ML pipelines. Building ML pipelines like this is great, as it allows you to optimize the process and be able to continuously innovate and improve your results. A feature store is going to make your project move faster, lead to more accurate results, and can even make it easier when building ML pipelines.

How This ML Pipeline Helps Things

Ultimately, the point of this ML pipeline is to have a feature store that helps your machine learning pipeline work even better. By doing things this way, you have the option of personalizing your machine learning models, and you can even continuously improve things over time. You no longer have to worry about your model drifting, as you can move features in and out as you rebuild your model periodically. The price of rebuilding your model also doesn’t matter, as you get many benefits that far outweigh that price. It is the optimal way for organizations to build models that work well.