ML-Powered IoT: From Warehouse Data to Prod Intelligence in Real Time
summary
Warehouse systems produce multiple terabytes of noisy sensor logs and show examples like 10,000 failed transactions per month, so the claim is: get 80–95% usable accuracy in production vs current ~50–60% by combining rolling windows and time‑series imputation, plus multi‑sensor fusion and metadata features. The stack uses containerization, model versioning, vector DB cosine‑similarity serving, continuous monitoring and deployment patterns (shadow, canary, A/B) and chooses edge, cloud or hybrid to balance sub‑ms latency against cloud accuracy.
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chapters (17)
- 0:00Welcome & Elevator Pitch: ML + IoT for Real-Time Production Intelligence
- 0:12Meet the Speaker: Digital Supply Chain IT at Honeywell
- 0:35The Real Problem: Tons of Warehouse Data, Little Actionable Insight
- 2:26Why Traditional ML Fails in Production (Data, Integration, Performance)
- 3:41Deep Dive: Data Quality Issues—Sensor Failures, Drift, Interference
- 4:30Integration Headaches: APIs, Latency, and Sync Problems
- 5:33From Raw IoT Data to ML Features: Rolling Windows, Events, Anomalies
- 7:51Handling Messy/Missing Data: Imputation, Sensor Fusion, Metadata
- 9:35MLOps Architecture for Scale: Containers, Versioning, Monitoring
- 14:08Safer Go-Live: Shadow Mode, Canary Releases, A/B Tests, Rollouts
- 15:41Model Drift Reality: Detection, Retraining, and Staying Current
- 16:35Edge vs Cloud vs Hybrid ML: Latency vs Accuracy Tradeoffs
- 18:26High-Value Use Cases: Predictive Maintenance, Forecasting, Smart Picking
- 19:57Responsible AI in Operations: Fairness, Explainability, Validation
- 23:01Trends Reshaping Warehouses: AutoML, 5G, Green/Lean Computing
- 26:04Lessons from the Field: What Makes Production ML Projects Win
- 27:38From Insights to Implementation: Practical Next Steps & Closing
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