Mage Blog

Using machine learning and artificial intelligence to integrate product recommendations gives the most accurate predictions for successful advertising, eliminates guesswork in creating campaigns, and creates magical customer experiences. 

Using machine learning (ML) to battle “Nigerian princes,” hackers, and customers abusing chargeback requests in order to reduce unnecessary losses.

Creating measurable strategies to ensure customer retention, and taking data-driven actions to prevent churn, is key to thriving in the ever-growing, competitive ecommerce market.

Why aren’t prospects doing what we want? Figure out how we can use ML to show viewers what they’re searching for and get them to buy immediately.

Using AI/ML, e-commerce businesses can integrate intelligent product recommendation engines to boost revenue, increase ROI, and improve the customer experience.

Marketing budgets fly away in today’s competitive bid for ad space. Ensure every dollar is spent wisely by using ML to customize marketing campaigns and optimize your business’ return of ad spend.

Wall street banks use a variety of tools when making market predictions. One of those tools is machine learning software. Using Mage to build a model that can estimate next-day prices can be another asset for individuals wanting to make predictions.

Is your model performing better than expected? Double-check if there’s data leakage that is inflating the accuracy and incorrectly evaluating your model performance!

In this Mage Academy lesson, we’ll be going over how to evaluate ranking models built for recommendation & personalization using mean average precision at K.

In this Mage Academy guide, we’ll go over how to evaluate a machine learning (ML) model and learn how to improve the model’s performance.