Modern C# / .NET Microservices with Docker, Kubernetes & AI

Install Visual Studio, Docker Desktop, and Azure CLI to set up essential development and deployment tools. Configure Docker and Azure credentials and run with WSL2 on Windows 10 or 11.

Discover how microservices form independently deployable units built around business capabilities, each with its own data, communicating via HTTP APIs or message brokers, and enabling team ownership and resilience.

Design and implement microservices that scale by decomposing functionality into focused services, including a user state source of truth, isolated authentication, resilient order processing, and asynchronous analytics and notifications.

Explore the trade-offs between monolith and microservices architectures, focusing on simplicity, tight user interface and shared data access in monoliths, versus independent deployment and diverse technologies in microservices.

Avoid microservices where overhead exceeds value, such as simple apps or tightly coupled services. Start with monoliths or simpler deployment models, then evaluate needs and evolve services independently.

Compare stateless and stateful services, noting stateless requests carry all data and scale across instances, while stateful services rely on persistent storage and may require instance affinity for consistency.

Implement single responsibility and loose coupling by separating user, order, payment, and search services with clear API contracts, enabling independent evolution, deployment, and contained failures.

Examine api gateway patterns from simple routing and load balancing to backend for frontend and client-specific APIs, enabling service-to-service communication, aggregation, data transformation, and reduced overfetching.

Decentralize data ownership by letting each service own its own database, eliminating shared schemas and enabling independent user, order, and payment workflows while reducing coupling and promoting polyglot persistence.

Explore service-to-service communication patterns, including synchronous REST API calls over HTTP and RPC with protocol buffers, asynchronous messaging with queues and publish-subscribe streams for real-time processing in loosely coupled systems.

Explore how LLM services fit into modern microservice architectures using direct API calls, API gateways, and message queues, enabling scalable, decoupled, and consistent AI capabilities across services.

Explain how Docker images serve as fixed, layered blueprints pushed to registries, while containers provide writable, disposable running instances connected to networks and volumes.

Explore how Docker layers drive fast, repeatable builds by caching and separating build and runtime stages, with immutable layers and shared base images reducing image size and disk usage.

Discover how docker volumes persist data and networks enable isolated, predictable communication for microservices, and how named volumes, bind mounts, and bridge or host networks support service discovery.

Understand how ports and networks are exposed in containerized systems, using port mapping, host and bridge networking, private communication, and docker compose for secure connectivity.

Adopt Docker best practices to reduce risk and friction by using smaller base images, limiting dependencies, copying only compiled output, and enabling health checks and non-root execution.

Optimize dockerfiles to improve security, performance, and deployment speed by excluding build tools, including only runtime dependencies, and using slim runtime images.

Debug Docker issues by inspecting container logs, streaming real-time output, and executing commands inside running containers to diagnose startup failures and validate the composed configuration.

Enforce container security basics by running as a non-root user, dropping capabilities, using a read-only file system, and regularly scanning base images to block vulnerabilities in deployment.

Master YAML basics for configuring microservices, using Docker Compose to define services, networks, and volumes in multi-container apps, with explicit data types, indentation, and key-value mappings.

Define multiple services with docker compose to orchestrate containers as one application. Use declarative configuration, startup sequencing, and port mapping for data service and api gateway coordination.

Define docker compose networks declaratively to connect containers and reduce surprises as services evolve. Explore bridge, host, and non-network options, custom networks, and front-end back-end isolation with service-name discovery.

Explore how environment variables drive runtime behavior in containerized apps, including ASP.NET Core apps, enabling consistent configurations with Docker Compose and .env files while injecting connection strings and feature flags.

Scale your services with Docker Compose by running multiple replicas for horizontal scaling, applying resource limits, and dynamically adjusting capacity to distribute traffic across replicas.

Configure Docker Compose logs per service by selecting a logging driver. Balance retention with rotation and structured formats, using json default, or syslog and journal.

Connects an llm service with other components in Docker Compose using direct service calls, dependency injection, and asynchronous messaging with RabbitMQ, plus environment configuration and health checks.

Diagnose docker-compose failures in containerized environments by recognizing common patterns such as deployment failures, image pull errors, port conflicts, and volume mounting issues. Apply a structured troubleshooting sequence—check logs, validate configuration, and change one variable at a time—to resolve connectivity, networking, and service-reference problems.

A message broker acts as an intermediary that routes messages from producers to consumers, enabling loose coupling, reliable delivery, and scalable, independent producer and consumer growth.

Queues coordinate work in distributed systems under load and growth, acting as a buffer between producers and consumers to protect downstream services from overload and enable loose coupling.

Harness AMQP, the open standard for message-oriented middleware, to enable reliable, loosely coupled delivery through exchanges, bindings, and queues between producers and consumers in microservices.

This lecture explains how routing decisions in a message broker direct messages to the right consumers through exchanges, queues, and routing keys, enabling delivery via direct, broadcast, or pattern-based routing.

Install rabbitmq in Docker creates a controlled, repeatable messaging setup for microservices. Docker Compose runs rabbitmq with ports 5672 and 15672, enabling asynchronous, broker-based communication and resilience.

Explore how the producer-consumer pattern enables asynchronous communication through a broker, using a RabbitMQ exchange to decouple producers and consumers, queue messages, and scale independently.

Publish order-created events with RabbitMQ to enable asynchronous, loosely coupled microservices and resilience. Order service emits events with id and timestamp, and a consumer processes them asynchronously, avoiding blocking flow.

Manage failures in distributed systems using retry logic and a dead-letter queue. Apply exponential back-off to govern retries and route failed messages for manual inspection, often with a circuit breaker.

Design for failure emphasizes resilience in microservices by isolating faults, applying timeouts, controlled retries with circuit breakers, bulkhead isolation, and graceful degradation to prevent cascading outages.

Adopt bulkhead isolation by isolating services into separate resource pools to limit the blast radius, prevent cascading failures, and keep core operations running with controlled degradation.

Explore the circuit breaker pattern for microservices: fail fast, limit calls to unhealthy dependencies, monitor response times and error rates, and automatically transition between open, testing, and closed states.

Explore a workflow where API gateway publishes events to a queue, a worker processes orders, and an AI service analyzes inventory to publish recommendations, highlighting decoupling, resilience, and load leveling.

Kubernetes is an orchestration layer that coordinates containers at scale with a control plane enforcing desired state and auto-correcting drift, grouping containers into pods for shared lifecycle, networking, and resources.

Explore Kubernetes primitives: pods, deployments, and services, and learn how pods group containers, enable rolling updates and scaling, and provide stable networking with IPs and DNS.

Write Kubernetes manifests as declarative configuration that defines apiVersion, kind, metadata, and specs like replicas and pod templates, stored in version control.

Understand how Kubernetes uses ConfigMaps for configuration and Secrets for sensitive data. Store settings as key-value pairs and consume them as files or environment variables, with secrets encrypted at rest.

Scale horizontally by adding replicas to distribute load and grow capacity as demand rises, with Kubernetes' horizontal pod autoscaler adjusting pods based on cpu, memory, network i/o, and queue length.

Verify the Kubernetes context and readiness, isolate microservices in a dedicated namespace, deploy each service with a manifest, externalize config with config maps, protect secrets, and enable health checks.

Define infrastructure with machine-readable files to replace manual configuration with declarative, repeatable definitions across environments. Use Terraform, Ansible, and Troposphere to provision, version, and orchestrate resources with automation.

Configure ingress to route external traffic to internal services, centralizing load balancing and ssl termination via an ingress controller that adapts to domain and path-based routing.

Deploy AI workloads with explicit memory and CPU planning to ensure stability, health checks, and scalable performance, accounting for model loading, warm-up, and sustained processing.

Explore AKS and EKS as cloud-managed Kubernetes that offload control plane and node management, enabling automated upgrades, scaling, load balancing, and secure access for high-availability deployments.

Learn how Helm packages Kubernetes resources into charts for repeatable deployments. Compare Helm to traditional package managers, using versioned charts, repositories, hooks, and fast rollbacks for upgrades.

Helm organizes a chart into a predictable directory layout—root, chart.yaml, values.yaml, charts, templates, and helpers—to enable consistent, reusable, and customizable deployments by rendering templates into Kubernetes manifests.

Explore how Helm templates and values render Kubernetes manifests with Go template syntax and Helm functions, placeholders for replica count and image tag, and defaults via values.yaml or command-line options.

Initialize a new chart directory with Helm, validate with lint, package into a versioned archive, and render templates locally to inspect Kubernetes manifests before deployment.

Manage release lifecycles in a Kubernetes cluster by deploying, upgrading, recovering, and removing applications using helm commands. Use helm install, list, upgrade, rollback, and uninstall to control releases.

Create and install a Helm chart by initializing the chart, reviewing core files, and applying default values; override specifics with dash set for replica count, enabling upgrades or rollbacks.

Learn how to upgrade an existing Helm release safely, applying only necessary changes such as a new image tag, while preserving history, release name, and rollback options.

Discover how container registries store and distribute images, including private registries with authentication, and how tagging and CI/CD enable versioned deployments across cloud platforms.

Compare private and public cloud networking to design secure, scalable microservices. Implement VPCs, subnets, security groups, routing, NAT gateways, and service discovery.

Explore how dns routing in microservices directs requests to services. Identify how dns resolves names to ip addresses, enabling dynamic discovery, health checks, time to live values, and scalable endpoints.

Distribute traffic across multiple service instances using load balancers to prevent overload and maintain stable response times as the system scales, with health checks and round-robin or ip hash.

Frame observability as foundational in distributed microservices, using json logs, traces, and metrics to monitor latency, throughput, and anomalies. Leverage CloudWatch or Azure Monitor for proactive alerts.

Master environment separation in cloud engineering to accelerate development while preserving stability, security, and trust across development, staging, and production with automation, dedicated databases, and environment-specific configurations.

Treat cost optimization as a continuous discipline alongside design and operations, balancing performance and efficiency while right-sizing resources, enabling dynamic scaling, and maintaining visibility into spending.

Explore REST API best practices to design clear, consistent, and correct APIs, using appropriate HTTP methods, descriptive URIs, explicit status codes, versioning, idempotence, and security.

Differentiate data transfer objects from domain models to define boundaries; DTOs move data with serialization attributes and no business logic, while domain models encapsulate rules and behavior.

Ensure data integrity by validating incoming data at the controller boundary using declarative attributes like required, string length, range, and email. Early, automated validation prevents malformed input from reaching services.

Learn to design consistent error responses with HTTP status codes, messages, and contextual details. Centralize error handling with multiple exception handlers and structured logging to enable monitoring and actionable outcomes.

Master api versioning strategies for microservices to evolve services without breaking consumers and maintain backward compatibility. Compare url-based, header-based, and media-type versioning, and apply semantic versioning with clear deprecation paths.

Learn how idempotency yields the same result on repeated requests in distributed systems. Understand that GET, PUT, and DELETE are idempotent; POST is not, and idempotency keys help prevent duplicates.