Feature Engineering in Machine Learning - neptune.ai

Companies are having difficulties with delivering and productionizing AI projects. This is painful and disappointing, and there are plenty of different solutions to problems like this. One of the major solutions is feature engineering. To get your data sorted out, analyze it, and get all necessary insights from it, you need to perform proper feature engineering. If you don't have good feature engineering in the front, you won't get much value out of the back. In the above chart, you can see that almost 82% of all the work done by data scientists is building, cleaning, organizing, and collecting data. This tells us why feature engineering is the most important aspect of machine learning -- it takes up a lot of time, and it has a big impact.