Accuracy tells you how many times the ML model was correct overall. Precision is how good the model is at predicting a specific category. Recall tells you how many times the model was able to detect a specific category.

Explore how Mage can help developers build flexible and scalable data pipelines. This article guides you through features that will help data engineers streamline their workflow design, enhance data processing, and ensure reliable data pipeline execution. By leveraging Mage Pro’s advanced tools, data engineers can simplify complex processes and achieve greater productivity in their projects.

Mage AI’s dynamic blocks revolutionize data pipelines by enabling adaptive, parallel processing. They automatically create multiple downstream blocks at runtime, allowing for flexible, scalable workflows that adjust to incoming data without manual intervention. The article provides a tutorial on implementing dynamic blocks, explores advanced features like “Reduce output” and dynamic SQL blocks, and highlights their applications in ETL processes, parallel processing, A/B testing, and multi-tenant systems. By mastering dynamic blocks, data engineers can create more efficient, adaptable pipelines that handle complex data processing scenarios with ease.

In this tutorial, we integrate dbt with Mage to create a data pipeline, moving data from a source to a PostgreSQL database and performing SQL transformations through staged models. By setting up Docker and PostgreSQL, and following a step-by-step process, we effectively manage data orchestration and analytics using Mage and dbt.