AppDynamics Monitoring for Machine Learning Applications

Deploying machine learning models to production is only the first step; maintaining their health, performance, and accuracy in real-time is where the real challenge begins. This text-based course introduces you to the essentials of application performance monitoring using AppDynamics, specifically tailored for machine learning workflows. You will transition from understanding basic monitoring concepts to actively tracking system health, detecting data drift, and diagnosing performance bottlenecks in live machine learning pipelines. What you'll learn: - Understand foundational APM concepts and how AppDynamics fits into modern MLOps pipelines. - Configure performance baselines to detect anomalies and latency issues in model predictions. - Monitor system resource utilization, including CPU, memory, and database calls for ML services. - Track key ML-specific metrics such as data drift, model degradation, and inference latency. - Analyze transaction snapshots to troubleshoot slow API requests and failed model runs. - Set up alerts and health rules to proactively identify production issues before they impact users. The course starts with essential terminology and the fundamentals of application performance monitoring, before guiding you through practical scenarios for configuring dashboards, analyzing metrics, and troubleshooting ML systems. Designed for beginner data scientists, software engineers, and aspiring MLOps professionals, this guide requires no prior AppDynamics experience. Start reading today to build reliable, high-performing machine learning deployments.