Performance Optimization in High-Volume Transaction Systems

Learn how to leverage machine learning for optimizing performance in high-volume transaction systems through this 14-minute conference talk from Conf42 ML 2026. Discover why traditional static threshold scaling approaches fail in reactive scenarios and explore predictive alternatives that can transform system performance. Examine the foundational architecture components including microservices, APIs, and Kafka, while understanding the critical scaling tradeoffs involved. Master the art of converting telemetry data into valuable training datasets for load forecasting and feature engineering. Explore advanced ML optimization strategies covering autoscaling, intelligent caching, and real-time system tuning capabilities. Compare ML-driven approaches against rule-based systems through concrete metrics including latency improvements, incident reduction, and cost savings analysis. Understand which machine learning models work best for different scenarios, including LSTM networks for time series prediction, anomaly detection algorithms, and regression techniques. Implement closed-loop optimization systems that create continuous feedback cycles from telemetry collection through predictions to automated scaling actions. Navigate production deployment challenges with focus on reliability, comprehensive monitoring, and risk mitigation strategies. Transition from reactive firefighting approaches to proactive forecasting methodologies that prevent performance issues before they occur. Examine real-world implementations using AWS Predictive Scaling and Azure Kubernetes Service with practical examples and best practices. Follow a complete end-to-end ML optimization pipeline walkthrough that demonstrates how all components integrate to deliver measurable performance improvements in production environments.