Monitoring AI Workloads on Azure: Performance and Reliability

Deploying AI models is only half the battle; ensuring they run reliably, efficiently, and cost-effectively in production is where the real challenge begins. As organizations increasingly rely on cloud-based AI, understanding how to observe and troubleshoot these workloads is a critical skill for modern technology professionals. This text-based course guides you through the core principles of AI observability and monitoring on Azure. You will transition from understanding basic system metrics to implementing advanced tracking for large language models, API latency, token usage, and system health, ensuring your intelligent applications remain robust and responsive. What you'll learn: - Understand the foundational concepts of AI observability, including metrics, logs, and distributed tracing. - Configure Azure Monitor to track the health, availability, and performance of Azure AI services. - Analyze key AI metrics such as API latency, token consumption, and request/response payloads. - Set up alerts and dashboards to proactively detect anomalies and performance degradation. - Explore AI model monitoring fundamentals using Azure AI Foundry to watch for data drift and quality issues. - Apply modern cost-tracking and resource-optimization strategies to keep your AI infrastructure efficient. You will start with essential terminology and the basics of cloud monitoring before moving step-by-step through practical configuration scenarios, log analysis, and alerting setups. Each concept is reinforced with clear written explanations and structured walkthroughs that you can read and apply at your own pace. This course is designed for IT professionals, developers, and system administrators who are new to AI operations on Azure. No prior experience with cloud monitoring is required to begin. Start reading today to build reliable, high-performing AI systems on Azure.