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Welcome + What You’ll Learn: ML for High-Volume Transaction Performance
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Classroom Contents
Performance Optimization in High-Volume Transaction Systems
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- 1 Welcome + What You’ll Learn: ML for High-Volume Transaction Performance
- 2 Meet the Speaker: Background in Scalable Microservices
- 3 Why Static Threshold Scaling Fails Reactive vs Predictive
- 4 Session Roadmap: Architecture, Telemetry, ML Strategies, Examples
- 5 Architecture Foundations: Microservices, APIs, Kafka & Scaling Tradeoffs
- 6 Telemetry as Training Data: Metrics, Features & Load Forecasting
- 7 ML Optimization Strategies: Autoscaling, Caching, Real-Time Tuning
- 8 ML vs Rule-Based Results: Latency, Incidents & Cost Savings
- 9 Which Models to Use: LSTM, Anomaly Detection & Regression
- 10 Closed-Loop Optimization: Telemetry → Predictions → Scaling Actions
- 11 Production Deployment Considerations: Reliability, Monitoring, Risk
- 12 Getting Started + Key Takeaways: From Firefighting to Forecasting
- 13 Real-World Cloud Examples: AWS Predictive Scaling & Azure Kubernetes
- 14 End-to-End ML Optimization Pipeline Diagram Walkthrough
- 15 Conclusion, Contact Info & Q&A
