AppDynamics Monitoring

Overview

AppDynamics Monitoring for Machine Learning Applications is a beginner-level course designed to equip data scientists, ML engineers, and DevOps professionals with the specialized monitoring skills needed for production ML systems. Unlike traditional applications, machine learning systems have unique failure modes, complex data dependencies, and business-critical performance requirements that demand sophisticated observability approaches. This course provides hands-on experience with AppDynamics' AI-powered monitoring platform, teaching learners to implement comprehensive monitoring solutions that capture both technical performance and business outcomes. Through real-world case studies, practical exercises, and advanced troubleshooting scenarios, learners will master the art of maintaining reliable, high-performing ML applications that deliver consistent business value in production environments.

Syllabus

Lesson 1: Introduction to AppDynamics for Machine Learning Applications

In this introductory lesson, learners will explore the fundamentals of AppDynamics monitoring platform and understand its unique application to machine learning environments. They will discover how modern ML applications require specialized monitoring approaches and learn about AppDynamics' architecture, core components, and AI-powered capabilities that make it particularly suited for data science workflows.

Lesson 2: Implementing AppDynamics Monitoring for ML Systems

In this hands-on lesson, learners will dive deep into the practical implementation of AppDynamics monitoring for machine learning systems. They will learn to map complex ML application flows, configure performance tracking for data science workflows, and set up health rules specifically designed for ML operations. Through real-world examples and guided exercises, learners will master the techniques needed to create comprehensive monitoring solutions that capture both infrastructure performance and ML-specific metrics critical for production success.

Lesson 3: Advanced Diagnostics and Optimization for ML Applications

In this advanced lesson, learners will master the sophisticated diagnostic and optimization capabilities of AppDynamics for machine learning applications. They will learn to identify performance bottlenecks, conduct root-cause analysis specific to ML systems, and implement optimization strategies that enhance both technical performance and business outcomes. Through real-world troubleshooting scenarios and hands-on optimization exercises, learners will develop the expertise needed to maintain high-performing ML applications in production environments and ensure their systems deliver consistent business value.