Mage Blog

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.

There's no magic equation to solve for the time to build an ML model. The answer depends on the data, type of model is being used, and acceptable accuracy.

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.

The future of business means AI. Onboarding talent capable of collecting data via machine learning is expensive but essential to stay relevant.