If you find this useful, please give it a ⭐ — it helps others discover this project! A Python tool that generates realistic test databases for an online computer & peripherals store, bundled with a ...

Deploying a new machine learning model to production is one of the most critical stages of the ML lifecycle. Even if a model performs well on validation and test datasets, directly replacing the ...

Abstract: In recent years, the growing complexity of database management systems (DBMSs) and the proliferation of SQL dialects have created significant challenges for database migration, federation, ...

In this tutorial, we build a complete, production-grade ML experimentation and deployment workflow using MLflow. We start by launching a dedicated MLflow Tracking Server with a structured backend and ...

Build, test, and deploy time-based incremental SQL models that process only new data intervals, minimizing compute costs for large datasets. A complete reference project demonstrating how to develop ...

Abstract: The advancements of Large language models (LLMs) have provided great opportunities to text-to-SQL tasks to overcome the main challenges to understand complex domain information and complex ...