DevOps Engineer from Scratch — Infrastructure and Automation

• From DevOps to DevSecOps: culture and areas of responsibility
• Architect's mindset: how to make technical decisions
• Shift-Left principle: security at the start, not at the end
• The three pillars of DevSecOps: people, processes, technology
• The C4 model: describing a system at different levels of abstraction
• ADR: recording architectural decisions
• Diagrams as code: versioning architecture
• Practice:Preparing a C4 diagram and the first ADR for the end-to-end project.

• Version control as the foundation of DevSecOps
• Branches, merges, and collaborative development workflows
• Pull Pull Requests and code reviews
• Main branch protection
• Secrets management: best practices
• Semantic versioning and Conventional Commits
• GitHub Actions: automation for every commit
• Hands-on practice: Building a monorepository with three microservices and a basic CI pipeline with linting

• Infrastructure as Code (IaC): core principles
• Terraform: infrastructure lifecycle management
• IaC security: configuration validation before deployment
• Policy-as-Code: implementing security requirements in code
• CI/CD: pipeline stages and workflows
• Zero-downtime deployment strategies
• GitOps: repository as the single source of truth
• Hands-on practice: Defining infrastructure for all three microservices in Terraform and configuring CD with OIDC authentication

• Monolith vs. microservices: use cases and architecture scenarios
• Synchronous communication: HTTP and gRPC
• Asynchronous communication: message queues
• Distributed system patterns: Saga, CQRS, Outbox
• Service Mesh и API Gateway
• Cloudflare Service Bindings: connecting Workers without HTTP
• Hands-on practice: Integrating a real LLM via AI Gateway, connecting services through Service Bindings, and configuring queues with a dead-letter queue

• IaaS, PaaS, SaaS, FaaS: practical differences and use cases
• Serverless и Edge Computing
• Stateful и stateless services
• Shared responsibility model in cloud security
• УCloud access management: roles and least privilege principles
• Cloudflare Zero Trust and Web Application Firewall (WAF)
• Hands-on practice: Securing the moderation interface with Cloudflare Access and implementing rate limiting via WAF

• Shift-Left Security: core principles and approach
• SAST: static application security testing
• SCA: dependency scanning and management
• CVE and SBOM: vulnerability tracking and software bill of materials
• DAST: dynamic application security testing
• Secret scanning: detecting leaked tokens and credentials
• OWASP Top 10: key vulnerability categories
• Hands-on practice: Integrating SAST, SCA, secret scanning, and DAST into the CI pipeline, and publishing an SBOM as a build artifact.

• Runtime security: real-time container monitoring
• Container image lifecycle: from build to deployment
• Admission Controllers and Linux hardening
• Centralized secrets management
• Supply Chain Security and SLSA framework
• Hands-on practice: Migrating secrets to Cloudflare Secrets, signing artifacts with Cosign, and integrating Falco for runtime monitoring

• Containers vs. virtual machines: key differences
• Dockerfile: layers, caching, and multi-stage builds
• Docker Compose: managing local multi-service environments
• Container registries: public and private
• Container security: best practices
• Hands-on practice: Containerizing all three microservices, building a local environment with Docker Compose, and integrating Trivy image scanning into the CD pipeline

• Kubernetes cluster architecture
• Core Kubernetes objects and components
• Pod health: probes, resource limits, and autoscaling
• RBAC and the principle of least privilege
• Security: Pod Security and Network Policies
• Helm: configuration and package management
• GitOps in Kubernetes: synchronization with ArgoCD or Flux
• Hands-on practice: Deploying three services in a local Kubernetes cluster, configuring RBAC, Network Policies, and GitOps using ArgoCD

• Symmetric and asymmetric encryption
• TLS and mTLS: handshake process and chain of trust
• PKI in DevSecOps: certificate management
• Hash functions: integrity verification
• Modern approaches: Forward Secrecy
• Hands-on practice: Enabling mTLS between services, configuring cert-manager, automating certificate rotation, and enforcing signature verification before deployment.

• MITRE ATTCK: resilience assessment framework
• CIS Benchmarks: security checks for Docker, Kubernetes, and Linux
• OWASP ASVS: application security verification standard
• Threat Modeling: the STRIDE methodology
• Compliance: ISO 27001, SOC 2, GDPR, and the role of DevSecOps
• Vulnerability management: CVE, CVSS, and prioritization
• Hands-on practice: Building a STRIDE threat model for the end-to-end project, auditing the cluster against CIS Benchmarks, and implementing OPA policies.

• Testing pyramid: Unit, Integration, E2E, and Contract testing
• Load testing: strategies and result interpretation
• SLO, SLA, and SLI: measuring reliability
• Chaos Engineering: intentional failures for resilience
• The three pillars of observability: Logs, Metrics, and Traces
• OpenTelemetry: a unified telemetry standard
• Distributed Tracing: tracking requests across multiple services
• Hands-on practice: Writing unit and integration tests for the end-to-end project, integrating OpenTelemetry, and running k6 load testing within the CI/CD pipeline.

• Prometheus: metrics collection and PromQL
• Grafana: dashboards and SLO-based alerts
• Loki: log aggregation and LogQL
• Alertmanager: alert routing and notification management
• Error budgets and burn rate monitoring
• Security Monitoring: audit logs and anomaly detection
• Incident response: from detection to postmortem analysis
• Hands-on practice: Deploying the Prometheus, Grafana, and Loki stack with Helm, configuring SLO alerts, and auto-generating runbooks.

• AI-assisted coding: autocompletion and reusable templates
• AI for Infrastructure as Code (IaC): Terraform and YAML generation
• AI for code reviews and remediation suggestions
• AI for documentation: README, Runbooks, and ADRs
• AI for incident analysis: logs and traces investigation AI agents in pipelines: automating actions based on scan results
• Local AI models: when to run AI without cloud services
• Hands-on practice: Integrating AI for PR reviews, using AI agents to analyze SAST/SCA reports, and auto-generating documentation

Yes, we cooperate with various government-funded programs that provide training support for different categories of applicants, including both unemployed and employed residents of Estonia. Submit an application to find out which government funding options may be available in your case.

Basic command-line skills and a general understanding of web applications are helpful. Programming experience is beneficial but not required — the course starts from the fundamentals and uses JavaScript.

You will build a chatbot system consisting of three microservices (Chat Worker, AI Worker, Moderation Worker) — from the first commit to a production-ready infrastructure. Each lab assignment adds a new layer: repositories, pipelines, Terraform, containers, cloud infrastructure management, security, and observability. By the end of the course, you will have a real portfolio project — not just a set of isolated exercises.

The schedule is flexible — classes take place 2–3 times per week, and course materials remain accessible to you permanently. Even if you miss a session, instructors will help you catch up.

You will receive an internationally recognized certificate from the MYEDU training center, as well as a certificate from the S-E School of Systems Analysis confirming your qualification. This document is valued by employers in the IT industry.