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Designing Robust ML Automation: Modularity, Continuous Learning & Aligned KPIs
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Classroom Contents
Machine Learning for Adaptive, Self-Optimizing Systems
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- 1 Welcome & Talk Overview: ML as the Intelligence Layer
- 2 Why Adaptive, Self-Optimizing Systems Matter in Industry
- 3 Core ML Techniques: RL, Predictive Models & Anomaly Detection
- 4 Manufacturing Wins: Throughput, Workflow Optimization & Predictive Maintenance
- 5 Logistics Automation: Smarter Routing, Load Balancing & Fewer Delays
- 6 Energy Operations: Forecasting Demand, Optimizing Distribution & Grid Anomalies
- 7 Self-Healing Automation: From Manual to Real-Time Anomaly Response
- 8 Predictive Maintenance Impact: Cutting Failures, Downtime & Costs
- 9 Interpretability & Explainable ML: Building Trust and Compliance
- 10 Hybrid AI + OT Integration: Architecture, Data Flow & Scalability
- 11 Operationalizing ML: Validation, Cross-Functional Teams & Feedback Loops
- 12 Designing Robust ML Automation: Modularity, Continuous Learning & Aligned KPIs
- 13 Choosing the Right KPIs: Throughput, Downtime Reduction & Energy Efficiency
- 14 ML Beyond Static Rules: Continuous Improvement & Adapting to Change
- 15 Closing Remarks & Thanks
