Cloud Operations Skills for DevOps Engineers Training Course

Course overview

DevOps engineers are often expected to operate cloud platforms that change every day: Kubernetes workloads scale unexpectedly, Terraform plans introduce risk, monitoring signals conflict, and an incident can require coordinated action across application, platform, security and network teams. This course prepares practitioners to run cloud services reliably rather than simply deploy them. Participants learn to define operational readiness, build observable services, automate repeatable recovery actions, and make evidence-based decisions about availability, capacity, security exposure and cloud spend.

The course covers cloud operating models, service-level objectives (SLOs), infrastructure as code, Kubernetes operations, observability, incident response, resilience engineering, identity controls and FinOps practices. Participants work with Terraform, Kubernetes, Prometheus and Grafana to provision controlled environments, inspect workload behaviour, create actionable alerts, investigate production-style faults and improve runbooks. They gain practical methods for designing dashboards, writing alert rules, planning capacity, conducting post-incident reviews, testing recovery procedures and governing operational changes through version-controlled automation.

Delivery combines instructor-led technical briefings with guided labs, team incident simulations and operational design workshops. Each participant builds an operational readiness pack for a representative cloud-hosted service: an architecture and dependency map, SLOs, dashboards, alert definitions, incident runbook, recovery test plan, Terraform control recommendations and a 90-day improvement backlog. This provides a usable template for improving a live platform after the course, while giving managers clear evidence of the operational controls their teams can establish.

The programme is best suited to DevOps engineers moving into platform ownership, as well as experienced engineers who need a disciplined operating model for cloud-native services. It is designed for organisations that need fewer avoidable incidents, faster restoration of service and stronger control over cloud delivery at scale.

Course objectives

By the end of this course, participants will be able to:

• Define service-level indicators, SLOs and error budgets for a cloud-hosted application
• Build Terraform modules and remote-state controls for repeatable infrastructure changes
• Operate Kubernetes workloads using health probes, resource requests, limits and rollout strategies
• Configure Prometheus metrics, recording rules and alert rules for actionable service monitoring
• Create Grafana dashboards that connect user-facing symptoms to platform dependencies
• Execute a structured incident response using triage, escalation, communications and incident timelines
• Produce a recovery test plan covering backup validation, failover steps and recovery time objectives
• Deliver an operational readiness pack containing runbooks, SLOs, dashboards and a prioritised improvement backlog

Benefits of attending

Target competencies

Who should attend

• DevOps Engineers — who must operate automated cloud delivery pipelines and production services
• Platform Engineers — who need to provide reliable, observable internal platforms for development teams
• Site Reliability Engineers — who need practical controls for SLOs, incident response and resilience testing
• Cloud Engineers — who manage cloud infrastructure and need stronger operational governance
• Infrastructure Automation Engineers — who need to turn Terraform-based provisioning into supportable operations
• Engineering Team Leads — who must reduce operational risk while improving deployment and recovery performance

Requirements and prerequisites

Participants should have hands-on experience deploying or supporting applications in at least one public-cloud or cloud-native environment. They should understand Linux command-line basics, networking concepts such as DNS, HTTP, TCP/IP and load balancing, and source control workflows with Git. Prior exposure to containers, YAML and Terraform configuration is strongly recommended, along with the ability to read application logs and interpret basic CPU, memory and latency metrics. Participants do not need prior Kubernetes administration certification, Prometheus expertise, advanced programming skills or experience with a specific cloud provider.

Training methodology

Instructor-led sessions establish the operating principles behind each practice, then participants apply them in a cloud-native service scenario. Guided labs use Terraform, Kubernetes, Prometheus and Grafana to provision, observe and troubleshoot workloads. Teams work through alert-noise analysis, deployment failure investigation and a timed incident simulation, making decisions from incomplete operational evidence. Daily reviews connect technical choices to reliability, recovery and cost outcomes. The final workshop converts lab outputs into an operational readiness pack and a 90-day improvement plan for a participant-selected service.

Course outline

Day 1: Cloud operations operating model

• Cloud operations responsibilities across DevOps, SRE, platform and security teams
• Service ownership boundaries and operational handover criteria
• Production readiness review checklists and evidence requirements
• Critical user journeys and dependency mapping
• Availability, latency, durability and recoverability service attributes
• Operational risk registers and risk-based prioritisation
• Change failure rate, mean time to restore and reliability metrics

Workshop: Participants map a representative cloud service, identify its dependencies and produce a production-readiness gap assessment.

Day 2: Infrastructure as code operations

• Terraform module design for reusable cloud infrastructure
• Remote state, state locking and workspace strategies
• Terraform plan review and policy-based change approval
• Variable management and environment-specific configuration
• Drift detection and reconciliation workflows
• Safe infrastructure rollout, rollback and destroy protection
• Version control branching and pull-request controls for infrastructure

Workshop: Participants review a flawed Terraform change set and produce a controlled deployment and rollback procedure.

Day 3: Kubernetes workload reliability

• Kubernetes control plane and worker-node operational responsibilities
• Deployments, StatefulSets and DaemonSets for workload management
• Readiness, liveness and startup probes
• Resource requests, limits and quality-of-service classes
• Horizontal Pod Autoscaling and cluster capacity considerations
• Rolling updates, canary releases and deployment rollback
• Network policies, namespaces and workload isolation

Workshop: Participants diagnose an unstable Kubernetes application and produce corrected workload manifests and resource settings.

Day 4: Observability design and metrics

• The metrics, logs and traces observability model
• Golden signals and RED method service indicators
• Prometheus metric types, labels and cardinality management
• PromQL queries for latency, error rate and saturation analysis
• Recording rules for efficient operational dashboards
• Grafana dashboard variables, panels and annotations
• Dashboard design for executives, on-call engineers and service owners

Workshop: Participants build a Grafana service dashboard from Prometheus metrics and document the decisions it supports.

Day 5: Alert engineering and SLOs

• Service-level indicators and service-level objective design
• Error budgets and reliability-versus-delivery decisions
• Multi-window, multi-burn-rate alerting patterns
• Symptom-based alerts versus cause-based alerts
• Alert severity, ownership, routing and escalation policies
• Alert fatigue analysis and signal-to-noise improvement
• SLO review cadences and reliability reporting

Workshop: Participants define SLOs and create a set of Prometheus alert rules for a customer-facing service.

Day 6: Incident response and problem management

• Incident command roles and decision authority
• Incident triage, severity classification and initial containment
• Technical investigation using timelines and hypothesis testing
• Stakeholder communication templates and update cadence
• Escalation paths across application, cloud and vendor teams
• Blameless post-incident review facilitation
• Corrective-action tracking and recurring-incident prevention

Workshop: Teams run a timed cloud-service incident simulation and produce an incident timeline, communications log and corrective-action list.

Day 7: Resilience, backup and disaster recovery

• Failure modes, blast radius and single-point-of-failure analysis
• Recovery time objectives and recovery point objectives
• Backup scope, retention, encryption and restoration validation
• Multi-zone and multi-region resilience patterns
• Database replication and data-consistency trade-offs
• Failover runbooks and dependency sequencing
• Game days and disaster-recovery test design

Workshop: Participants create a recovery test plan for a cloud application, including RTO, RPO, failover steps and validation evidence.

Day 8: Cloud security operations and governance

• Least-privilege identity and access management design
• Privileged access review and break-glass procedures
• Secrets management and rotation operating procedures
• Cloud audit logs and security event investigation
• Container image provenance and vulnerability response
• Configuration baselines and policy-as-code controls
• Operational evidence for audit and compliance reviews

Workshop: Participants investigate a simulated access-control finding and produce a remediation runbook with approval and verification steps.

Day 9: Capacity, performance and cloud cost operations

• Demand forecasting using workload and business indicators
• Capacity headroom and saturation threshold setting
• Load-testing objectives and performance baseline interpretation
• Rightsizing compute, storage and Kubernetes resource allocations
• Cost allocation tags, labels and ownership models
• FinOps unit economics and cost anomaly investigation
• Performance-versus-cost trade-off decision records

Workshop: Participants analyse service utilisation and cost data to produce a rightsizing proposal with reliability safeguards.

Day 10: Operational readiness capstone

• Operational readiness pack structure and review criteria
• Architecture dependency maps and service ownership records
• SLO, dashboard and alert coverage validation
• Runbook quality checks and escalation completeness
• Change, release and rollback operational controls
• Ninety-day reliability improvement backlog prioritisation
• Leadership briefing of operational risk and investment needs

Workshop: Participants present their completed operational readiness pack and 90-day improvement plan for peer and instructor review.

Tools & standards covered

Terraform, Kubernetes, Prometheus, Grafana

A typical training day

08:30 – 10:30	First session
10:30 – 10:45	Refreshment break
10:45 – 12:30	Second session
12:30 – 13:30	Lunch and networking
13:30 – 15:00	Third session
15:00 – 15:15	Refreshment break
15:15 – 16:30	Workshop and daily review

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions