Developers

Backfilling integrates historical data into data pipelines, ensuring completeness and mitigating failures. Mage provides a no-code UI and custom coding options to streamline backfilling for robust, resilient data pipelines.

Mage now supports a suite of DuckDB & MotherDuck features— from reading and writing DuckDB databases to executing dbt with dbt-duckdb!

Accuracy tells overall correctness. Precision is specific to a category. Recall tells you successful detection of a specific category.

Clean data sets the foundation for the success model to be built. Ensuring your AI/ML models are build with clean data can save time and money.

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.

In this Mage Academy lesson, we’ll build a model to classify the genre of a song based on the given attributes.

In this blog, we’ll explore discrete and categorical features in the Telco Customer Churn dataset using univariate graphical methods.

A picture is worth a thousand words! Learn how to visualize the story told by your data.

Once you have a model, how do you know how well it works? Become Neo and enter the Matrix.

Learn how to import and export training models to share with the world!