Transforming Inventory Forecasting with Production ML

Learn how to revolutionize inventory management through production machine learning in this 16-minute conference talk from Conf42 ML 2026. Discover why traditional inventory forecasting fails due to volatility, manual planning processes, and common organizational barriers, then explore a comprehensive target reference architecture that transforms raw data into automated business decisions. Master data ingestion and governance techniques using Azure pipelines, data lakes, and quality controls, while building predictive signals through advanced feature engineering incorporating seasonality, promotional effects, and weather patterns. Understand model validation best practices including backtesting methodologies, accuracy metrics, and bias detection to ensure fair and reliable predictions. Explore deployment strategies and MLOps workflows covering CI/CD pipelines, model registries, and continuous monitoring systems. See how machine learning predictions translate into automated replenishment ordering systems that drive real business value. Examine practical applications through real-world scenarios in healthcare and logistics industries, demonstrating measurable business impact and return on investment from implementing production ML systems for inventory optimization.