Problem Statement:

Businesses require accurate predictions of future product usage across different segments to optimize inventory, resource allocation, and strategic planning

Objective:

Build a production-grade machine learning pipeline that forecasts future product usage across various segments, enabling actionable business decisions and ROI improvements

Process Pipeline:

• Data Ingestion from Azure Blob Storage/SQL
• Multi-segment Data Preparation using Azure Databricks
• MultiOutput Random Forest Training
• Model Registration and Deployment via Azure ML
• Automated Drift Monitoring and Retraining

Technologies & Models:

MultiOutput Random Forest Classifier, Azure ML, Azure Databricks, Spark ML

Key Outcomes:

Accurate Segment Forecasting, Strategic Planning Optimization, ROI Improvement