The 10 best Monitoring & Observability courses in 2026

This course focuses on designing governed, scalable lakehouse architectures that support AI-native data platforms. Learners translate AI workload requirements into data product SLOs, compare open table formats, design ingestion and replay strategies, manage schema evolution, support reproducibility, and define observability signals for freshness, latency, throughput, and cost. The course emphasizes architecture and operational patterns rather than vendor-specific platform administration. By the end of the course, learners can explain when a lakehouse is preferable to a warehouse or data lake f

Building resilient systems requires more than knowing individual tools—it demands the ability to design architectures that anticipate failure and recover effectively. In this intermediate course, you will learn how to apply resilience engineering principles to modern distributed systems, focusing on high availability, fault tolerance, and disaster recovery planning. You will analyze how and why systems fail, identify hidden risks in system architecture, and design strategies that improve uptime and reliability. The course connects key concepts such as load balancing, redundancy, observability

Learn to deploy ML models to production using the Sovereign Rust Stack—a pure Rust implementation with zero Python runtime dependencies. This hands-on course teaches you to work with three critical model formats (GGUF, SafeTensors, APR), implement MLOps pipelines with CI/CD and observability, and deploy models across GPU, CPU, WebAssembly, and edge targets. Through real-world projects including a Python-to-Rust transpiler (Depyler), browser-based speech recognition (Whisper.apr), and LLM inference benchmarking (Qwen), you'll master format conversion, cryptographic model signing, and performan

This Advanced Monitoring and Logging Training develops strong skills in designing, implementing, and managing modern monitoring and logging systems using Prometheus, Grafana, and the ELK Stack. You learn monitoring fundamentals, metrics collection, alerting, visualization, and centralized logging through hands-on labs and real-world demos. The course covers PromQL, instrumentation, Alertmanager, dashboard creation, log ingestion, and automation. It shows how to improve visibility, detect issues early, and maintain reliable, production-ready environments. By the end of this course, you will be

Take your Spring Boot skills to the next level by learning how to deploy, scale, and monitor real-world applications using tools like Docker, AWS ECS, and Spring Security. In this hands-on course, you'll apply essential DevOps practices—CI/CD, containerization, and observability—to move confidently from local development to production-ready deployment. In the first module, you’ll explore modern deployment workflows, external configuration, and container fundamentals. You’ll also understand how Docker integrates into Spring Boot development. The second module guides you through building effici

This course teaches how to deploy and monitor microservices in cloud-native environments using Kubernetes. Mastering these skills is critical for building scalable, observable, and maintainable distributed systems. Learners will deploy microservices to Kubernetes, use service meshes for observability, implement centralized logging with the EFK stack, and monitor system performance. These exercises give hands-on experience in real-world deployments. The course combines theoretical concepts with practical applications, covering Kubernetes features, native compiled Java microservices, and cross

Welcome to the two-part course on Logging, Monitoring, and Observability in Google Cloud. The core operations tools in Google Cloud break down into two major categories. The operations-focused components and the application performance management tools. This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring. After taking this course, it is suggested that you complete part 2, Observability in Google Cloud, to learn about the available application performance management tools.

This course helps you advance your skills in analytics engineering and gives you the practical abilities required to build scalable and reliable dbt projects. You will begin by strengthening your understanding of reusable SQL development with Jinja and macros and learn how to organize transformation logic for large data systems. From there, you will explore incremental models, snapshots, testing strategies, documentation practices, and core observability concepts that support trustworthy analytics workflows. The course concludes with collaboration techniques and workflow automation, where you

This course equips professionals with the skills to effectively monitor systems using Prometheus, a leading open-source monitoring and alerting tool. Understanding observability and leveraging Prometheus is critical for maintaining high-performing, reliable IT infrastructures. Learners will gain hands-on experience deploying Prometheus, configuring service discovery, writing PromQL queries, and implementing alerts. Practical exercises ensure you can measure, analyze, and visualize system performance to support operational decisions. Unlike typical tutorials, this course blends theoretical kn

This Advanced Site Reliability Engineering Training builds strong expertise in designing, operating, and scaling highly reliable cloud systems using modern SRE and DevOps practices. You learn SLIs, SLOs, SLAs, error budgets, observability, incident management, alerting, RCA, CI CD, chaos engineering, Infrastructure as Code, and performance testing through hands on labs and real world demos using Prometheus, Grafana, Jenkins, Docker, Kubernetes, and Ansible. The course shows how to reduce toil, automate operations, improve resilience, and maintain production ready systems at scale. By the end