What does the Production Environment in DevOps course cover?
Production Environment in DevOps is covered here in 7 modules: Infrastructure as Code (IaC) Strategy and Implementation, CI/CD Pipeline Design for Production Safety, Production Monitoring and Observability and 4 more. The outline lists 42 specific topics, opening with selecting between declarative (e.g., Terraform) and imperative (e.g., Ansible) IaC tools based on team skill sets and change control requirements.
How do you approach Production Environment in DevOps step by step?
The work is sequenced in 7 stages. It starts with Infrastructure as Code (IaC) Strategy and Implementation, moves through CI/CD Pipeline Design for Production Safety and Production Monitoring and Observability, and ends at Capacity Planning and Performance Optimization. Each stage carries its own topic list, so the sequence is followed rather than summarised.
What is in Module 1 of the Production Environment in DevOps course?
Module 1 is Infrastructure as Code (IaC) Strategy and Implementation. It works through selecting between declarative (e.g., Terraform) and imperative (e.g., Ansible) IaC tools based on team skill sets and change control requirements., designing reusable, parameterized IaC modules with versioned dependencies to support consistent multi-environment deployments., enforcing IaC peer review policies in pull requests to prevent configuration drift and unauthorized resource provisioning.
How is the Production Environment in DevOps course delivered?
The Production Environment in DevOps course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.
How much does the Production Environment in DevOps course cost?
The Production Environment in DevOps course is $250 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.
Closely related courses: IT Environment in DevOps, Test Environment in DevOps, Environment Provisioning in DevOps, Environment Configuration in DevOps.
More answers: what you get with every course, refund policy, all help answers.
This curriculum spans the technical and procedural rigor of a multi-workshop DevOps transformation program, addressing the same infrastructure, security, and operational disciplines applied in enterprise-scale production environments.
Module 1: Infrastructure as Code (IaC) Strategy and Implementation
- Selecting between declarative (e.g., Terraform) and imperative (e.g., Ansible) IaC tools based on team skill sets and change control requirements.
- Designing reusable, parameterized IaC modules with versioned dependencies to support consistent multi-environment deployments.
- Enforcing IaC peer review policies in pull requests to prevent configuration drift and unauthorized resource provisioning.
- Integrating IaC scanning tools (e.g., Checkov, tfsec) into CI pipelines to detect security misconfigurations before deployment.
- Managing state files securely in remote backends with role-based access and audit logging, avoiding local state in production workflows.
- Planning for immutable infrastructure patterns versus mutable updates when managing long-running production workloads.
Module 2: CI/CD Pipeline Design for Production Safety
- Implementing canary deployments with traffic shifting via service mesh or load balancer rules to reduce blast radius.
- Configuring automated rollback triggers based on health checks, error rates, or latency thresholds in monitoring systems.
- Requiring manual approval gates for production promotions while maintaining audit trails and role-based authorization.
- Enforcing artifact immutability by promoting the same build artifact across environments using versioned identifiers.
- Securing pipeline secrets using dedicated secret management tools (e.g., HashiCorp Vault) instead of environment variables.
- Isolating production pipeline stages with network segmentation and minimal privilege service accounts.
Module 3: Production Monitoring and Observability
- Defining SLOs and error budgets to guide incident response and feature release pacing in production systems.
- Instrumenting distributed tracing across microservices using context propagation to diagnose latency bottlenecks.
- Configuring alerting rules to minimize noise by focusing on user-impacting metrics rather than infrastructure-level thresholds.
- Centralizing logs with structured formatting and retention policies aligned with compliance requirements.
- Correlating metrics, logs, and traces using unique request identifiers to accelerate root cause analysis.
- Validating monitoring coverage during deployment by verifying new services are auto-discovered and scraped.
Module 4: Security and Compliance in Production Systems
- Enforcing runtime security policies using OPA or Kyverno to block non-compliant container deployments.
- Implementing network policies in Kubernetes to restrict pod-to-pod communication based on least privilege.
- Conducting regular vulnerability scans of container images and patching within defined SLAs for critical findings.
- Rotating production secrets and certificates automatically using tools like Vault or AWS Secrets Manager.
- Enabling audit logging for all production API calls and storing logs in immutable, write-once storage.
- Mapping controls to compliance frameworks (e.g., SOC 2, ISO 27001) and automating evidence collection.
Module 5: Disaster Recovery and High Availability Planning
- Defining RPO and RTO targets for each production service and designing backup strategies accordingly.
- Testing failover procedures regularly in staging environments to validate cross-region redundancy.
- Automating backup validation by restoring snapshots to isolated environments and verifying data integrity.
- Architecting stateful services with distributed databases that support multi-region replication and quorum reads.
- Documenting and versioning runbooks for critical failure scenarios, including escalation paths and communication protocols.
- Using chaos engineering tools to inject failures (e.g., node shutdown, latency spikes) and validate system resilience.
Module 6: Change Management and Production Governance
- Requiring change advisory board (CAB) review for high-risk changes while allowing low-risk changes to proceed via automated checks.
- Tracking all production changes in a centralized system with metadata such as change owner, impact level, and rollback plan.
- Enforcing a production change freeze window during peak business periods or critical events.
- Integrating deployment tracking with ITSM tools to maintain alignment with enterprise change management processes.
- Conducting post-implementation reviews for failed or impactful changes to update policies and prevent recurrence.
- Standardizing change templates to ensure consistent risk assessment and stakeholder notification.
Module 7: Capacity Planning and Performance Optimization
- Forecasting resource demand using historical usage trends and business growth projections to guide scaling decisions.
- Right-sizing cloud instances based on actual CPU, memory, and I/O utilization rather than default configurations.
- Implementing autoscaling policies with cooldown periods and predictive scaling to handle traffic spikes efficiently.
- Monitoring for resource contention in shared environments (e.g., noisy neighbors in multi-tenant clusters).
- Optimizing database performance through indexing strategies, query tuning, and read replica placement.
- Conducting load testing in pre-production environments with production-like data and traffic patterns.