Distinguishing data engineering from Machine Learning pipelines.

Data engineering and machine learning pipelines serve distinct yet interconnected roles in data-driven organizations. Data pipelines focus on collecting, cleaning, integrating, and storing data, forming the foundation for informed decision-making. Machine learning pipelines, in contrast, encompass data cleaning, feature engineering, model training, evaluation, deployment, and monitoring, enabling automation of ML model development. While data pipelines follow a linear path from data source to storage, ML pipelines operate in a circular fashion, iterating from data to model deployment. Both require adaptable computational resources to handle variable workloads. Understanding these differences is crucial for building and maintaining effective pipelines, ensuring timely, accurate data for informed decisions and improved performance.