This module teaches you to implement distributed tracing for AI microservices. You'll instrument services with OpenTelemetry, visualize traces in Jaeger, and identify latency outliers affecting prediction pipeline performance.

This module teaches you to build production-grade inference container images: multi-stage Dockerfiles that separate build tooling from a minimal runtime image, deliberate base image selection, and a Trivy scan-and-remediate workflow that clears high and critical CVEs before push.

This module teaches you to analyze and optimize Kubernetes resource allocations. You'll use metrics-server and kubectl to measure actual utilization, then adjust requests and limits to improve efficiency without impacting performance.

This module teaches the deployment of AI inference to edge devices using Azure IoT Edge. It covers creating deployment manifests, configuring ONNX Runtime modules, and implementing offline buffering so predictions continue when cloud connectivity is interrupted.

This module teaches evaluation and selection of communication protocols for edge-to-cloud data transfer. It compares MQTT, AMQP, and HTTPS under constrained network conditions, measures performance differences, and produces a protocol recommendation with documented rationale.

This module teaches you to implement gRPC-based communication for AI microservices. You'll design protobuf schemas, generate client/server stubs, and refactor services from REST to gRPC for improved efficiency and type safety.

This module teaches you to build resilient distributed systems that handle failures gracefully. You'll implement retry logic with exponential backoff, use chaos engineering to simulate failures, and validate your resilience mechanisms under realistic conditions.

This module teaches you to implement Blue-Green deployments on Kubernetes. You'll create parallel deployments, switch traffic using Service selectors, and execute zero-downtime cutovers with instant rollback capability.

This module teaches you to evaluate deployment strategies for edge environments with unique constraints. You'll compare Canary and Blue-Green approaches for edge scenarios, considering connectivity, rollback complexity, and risk management.

Learn to use generative AI tools to accelerate infrastructure code development for containerized AI workloads. You'll evaluate AI coding tools for enterprise use, apply AI assistants to generate Helm charts, Dockerfiles, Kubernetes manifests, and Terraform modules, then critically evaluate the output for correctness, security, and best practices. This module builds on your container orchestration skills to show how AI augments (not replaces) infrastructure expertise, while addressing the governance considerations platform engineers must own.

Apply container and edge orchestration skills to design and document a complete edge inference solution. Learners create deployment artifacts, configure offline capabilities, plan resilient communication, and produce an operational runbook enabling production deployment and maintenance.