Using your customer purchase data, you can build a machine learning model in Mage to estimate how valuable a user is over a finite period of time in the future.

DevEd is a new branch of technical content creation for product developers, focused on giving back to the community by creating quality content.

Amplitude is a product analytics platform that allows companies to better understand user behavior in their applications. Examples of data captured by Amplitude are event time, location, device model, click events, etc. With this ability, you will be able to add Amplitude as a data source in the Mage app.

Terraforming a planet requires large scale projects to inhabit other planets for survival. We’ll begin by terraforming datasets to calculate the cost of survival on the Titanic.

We’ll explore the different types of Artificial Intelligence (AI) and examples of how they are used today.

Having a continuous integration / continuous delivery (CI/CD) pipeline set up for each of our git repositories is an essential part of Mage’s software development life cycle. It allows us to greatly speed up the process of testing, building, and deploying our application without sacrificing quality assurance.

Datasets are columns and rows of data used to build and train an AI model. After uploading or adding data into Mage, you can now create an exact copy of that dataset or any dataset within your workspace.

Surfing through data is a quick and simple way to put all relevant information on the top. To go from a searching experience to a surfing experience, all it takes is a strong filtering, sorting, and grouping procedure.

Manufacturing plays a big role in today’s society. With the impact of the recent pandemic, the industry has shifted to a digital transformation and the oncoming of the industrial revolution 4.0. With the application of AI, companies have gained operational efficiency and increased production quality while reducing risk and improving safety.

Without good performance, machine learning (ML) models won’t provide much value in real life. We’ll introduce some common strategies to improve model performance including selecting the best algorithm, tuning model settings, and adding new features (aka feature engineering).