In recent years, the rapid growth of AI technologies has resulted in an increasing number of organizations adopting machine learning techniques to solve complex business problems. However, the process of building and deploying ML models at scale can be challenging, involving numerous complex steps such as data preparation, feature engineering, model training, and deployment.​

Various ML frameworks have emerged to address these challenges. This document will present a framework that provides a structured approach to managing ML projects, enabling teams to collaborate more efficiently, improve code quality, and automate various tasks. Like popular frameworks such as Kedro, Flyte, and SageMaker, this framework provides an opinionated structure to develop, maintain, and scale ML projects. The framework emphasizes modularity, reproducibility, and versioning, enabling teams to track changes easily and reproduce results.​

In the following sections, we will dive into this framework's key features and benefits, along with examples and best practices for implementing it in your ML projects.