The 6 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

Search and analyze data at scale with Elasticsearch, Logstash, and Kibana.

Monitor infrastructure and applications with Prometheus, Alertmanager, and Grafana dashboards.

Google's course on SLIs, SLOs, and error budgets — the foundation of SRE practice.