Inventory management is a crucial aspect of businesses dealing with tangible products, focusing on efficient organization and marketing based on thorough evaluation. Central to this is the concept of inventory turnover, which measures how often a company sells and replaces its stock within a given timeframe. Utilizing machine learning, particularly regression algorithms, can significantly enhance inventory management by predicting which products need restocking and thereby optimizing the inventory turnover ratio (ITR). The guide outlines how to employ Datagran to set up an ML pipeline using Decision Tree algorithms to manage inventory data, train models, and predict restocking needs, aiming to lower the ITR and better align with market trends. The process involves integrating data sources, setting up a SQL-based pipeline, and using Spark Regression to achieve real-time insights, ultimately facilitating faster and more accurate inventory decisions.