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Explore how to transform warehouse IoT data into actionable production intelligence through machine learning in this 29-minute conference talk by Dinesh Garg from Honeywell's Digital Supply Chain IT team. Learn why traditional ML approaches fail in production environments due to data quality issues, integration challenges, and performance constraints. Discover practical solutions for handling sensor failures, data drift, and interference while building robust MLOps architectures using containers, versioning, and monitoring systems. Master techniques for converting raw IoT data into meaningful ML features through rolling windows, event detection, and anomaly identification. Understand strategies for managing messy and missing data through imputation, sensor fusion, and metadata utilization. Examine deployment best practices including shadow mode testing, canary releases, A/B testing, and gradual rollouts to ensure safer production launches. Address model drift challenges with detection mechanisms, retraining protocols, and currency maintenance strategies. Compare edge, cloud, and hybrid ML architectures while evaluating latency versus accuracy tradeoffs. Explore high-value applications including predictive maintenance, demand forecasting, and intelligent picking systems. Delve into responsible AI practices covering fairness, explainability, and validation requirements for operational environments. Stay current with emerging trends such as AutoML, 5G connectivity, and sustainable computing approaches that are reshaping modern warehouses. Gain field-tested insights on what makes production ML projects successful and receive practical next steps for implementation.
