Data management architecture for ML

Depending on the scale of your ML initiatives, it is important to consider different data management architecture patterns to effectively support them.For small-scale ML projects characterized by limited data scope, a small team size, and minimal cross-functional dependencies, a purpose-built data pipeline tailored to meet the specific project requirements can be a suitable approach. For instance, if your project involves working with structured data sourced from an existing data warehouse and a publicly available dataset, you can consider developing a straightforward data pipeline. This pipeline would extract the necessary data from the data warehouse and public domain and store it in a dedicated storage location owned by the project team. This data extraction process can be scheduled as needed to facilitate further analysis and processing. The diagram below illustrates a simplified data management flow designed to support a small-scale ML project...