Mage Blog

Let’s take a look at the different steps to build a prediction model and go over the what, when, why, and how people accomplish them.

Responsible AI uses AI to empower individuals and put people’s values first. Its ethical deployment is essential to fight against algorithmic bias.

Many invest heavily into remarketing strategies with vendors, but what about building your own? Leverage existing consumer data for your next campaign.

A machine learning (ML) model is only as good as its input data. In a world of inequity, developers must ensure they build responsible models void of bias.

Apply feature engineering by converting time series data to numerical values for training machine learning models.

Artificial Intelligence is shifting recruiting from a human to largely computer-based process — from curating job descriptions to conducting interviews.

When collecting consumer data, there are times when you’re unable to retrieve all the data so you’ll want to “guestimate” what the data should be.

Machines have an easy time understanding numbers, but can't comprehend meaning. Learn techniques to scale numerical data to better grasp your data.

With the evolution of AI comes many tools for product developers. AI isn’t replacing your job, it’s making it easier and here’s how.

Technical design documents are detailed game plans for features or solutions to technical problems. They end up saving your team a lot of time and code.