Performance Optimization in High-Volume Transaction Systems
summary
Shows an end-to-end ML pipeline that ingests telemetry (CPU, latency, request rate, thread usage) and uses LSTM, anomaly detection and regression models to forecast load and trigger closed‑loop actions like predictive autoscaling, dynamic caching and realtime config tuning. Claims include 73% fewer performance incidents and 28% lower infrastructure cost from proactive scaling. Architecture details: Spring Boot microservices, Kafka event streams, Kubernetes pods, AWS Predictive Autoscaling and Azure AKS examples. Deployment notes stress monitoring, validation and risk mitigation for production models.
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chapters (15)
- 0:00Welcome + What You’ll Learn: ML for High-Volume Transaction Performance
- 0:37Meet the Speaker: Background in Scalable Microservices
- 1:36Why Static Threshold Scaling Fails (Reactive vs Predictive)
- 2:26Session Roadmap: Architecture, Telemetry, ML Strategies, Examples
- 2:48Architecture Foundations: Microservices, APIs, Kafka & Scaling Tradeoffs
- 4:40Telemetry as Training Data: Metrics, Features & Load Forecasting
- 5:45ML Optimization Strategies: Autoscaling, Caching, Real-Time Tuning
- 6:33ML vs Rule-Based Results: Latency, Incidents & Cost Savings
- 7:21Which Models to Use: LSTM, Anomaly Detection & Regression
- 8:49Closed-Loop Optimization: Telemetry → Predictions → Scaling Actions
- 9:47Production Deployment Considerations: Reliability, Monitoring, Risk
- 10:21Getting Started + Key Takeaways: From Firefighting to Forecasting
- 11:46Real-World Cloud Examples: AWS Predictive Scaling & Azure Kubernetes
- 12:16End-to-End ML Optimization Pipeline Diagram Walkthrough
- 13:04Conclusion, Contact Info & Q&A
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