Outcome-oriented pathways, not random lessons
Each pathway is designed around real role progression and hands-on practice. Detailed curriculum is shared with qualified learners after a short conversation to ensure a strong fit.
DevSecOps & Reliability
The DevSecOps & Reliability pathway is the most advanced and specialised programme in the CloudOps Academy portfolio. It is built for engineers who want to be the person organisations turn to when they need their systems secured, their pipelines hardened, and their services guaranteed to stay up. This pathway sits at the intersection of two disciplines that are becoming inseparable in the modern cloud era: security automation and reliability engineering.
DevSecOps engineers are among the highest-paid professionals in the technology industry — with average salaries of $138,000–$165,000 in the US — because they combine three skill sets most people keep separate: development, security, and operations. The DevSecOps market is projected to reach $41.6 billion by 2030, growing at 30% per year. Only 37% of IT leaders can find qualified talent. That skills gap is your opportunity.
Site Reliability Engineering (SRE) is the Google-born discipline that has transformed how technology companies think about uptime, incident response, and operational excellence. Netflix, Google, Amazon, Stripe, and every high-performing technology company operates on SRE principles. This pathway
Learning Outcomes
By the end of this programme, participants will be able to:
Design and implement a complete DevSecOps pipeline with automated security gates at every stage — SAST, DAST, SCA, container scanning, IaC scanning, and secrets detection
Build and operate a zero-trust cloud security architecture on AWS using IAM, SCPs, KMS, Secrets Manager, GuardDuty, Security Hub, WAF, and Inspector
Implement supply chain security using SLSA framework, SBOM generation, Sigstore/Cosign image signing, and provenance attestation
Design and enforce Kubernetes security using Pod Security Standards, OPA/Gatekeeper policies, Falco runtime detection, and network policies
Conduct security testing including DAST with OWASP ZAP, API security testing, penetration testing methodology, and threat modelling with STRIDE
Apply compliance-as-code principles to automate SOC 2, PCI-DSS, HIPAA, ISO 27001, and CIS Benchmark controls
Define SLIs, SLOs, and error budgets for services and use them to make data-driven reliability decisions
Build and operate a complete SRE observability stack with Prometheus, Grafana, Loki, OpenTelemetry, and distributed tracing
Design and execute chaos engineering experiments using AWS Fault Injection Simulator and Chaos Mesh to proactively find and fix reliability weaknesses
Lead incident response using structured incident command, runbooks, and blameless post-mortems
Eliminate toil through automation — writing Go or Python tooling that replaces manual operational work
Architect highly available, self-healing systems with multi-region failover, automated rollbacks, and circuit breakers
Endpoint security with SIEM platforms like Microsoft Sentinel or XDR with Wazuh
Observability & Monitoring Engineering
The Observability & Monitoring Engineering pathway is for engineers who want to be the person every team in the organisation turns to when nobody can figure out what is wrong. Observability engineers are the architects of visibility — the professionals who build the systems that tell the business exactly what its software is doing, when it breaks, why it broke, and how to fix it. In 2025, as distributed systems become more complex and the cost of downtime rises, this is one of the fastest-growing, highest-impact specialisations in cloud engineering.
This pathway is built around a simple but powerful premise: monitoring tells you something is wrong. Observability tells you why. Most organisations have monitoring — dashboards, alerts, and on-call rotations. Very few have genuine observability — the ability to ask arbitrary questions about their systems and get answers, even questions they did not anticipate needing to ask. This programme teaches participants to build both, at enterprise scale, across the most in-demand toolchains in the industry.
Learning Outcomes
By the end of this programme, participants will be able to:
Design and deploy a production-grade open-source LGTM observability stack (Loki, Grafana, Tempo, Mimir) on Kubernetes
Instrument applications in any language using OpenTelemetry SDKs and configure the OpenTelemetry Collector as an enterprise telemetry pipeline
Build production-grade Prometheus monitoring with PromQL expertise, recording rules, AlertManager routing, and Thanos for long-term scalability
Master Grafana deeply — advanced dashboard engineering, templating, alerting, Grafana OnCall, and dashboards-as-code with Grafonnet and Jsonnet
Implement and operate Datadog as a complete enterprise observability platform — APM, logs, metrics, RUM, Synthetics, and monitors-as-code with Terraform
Configure and operate Dynatrace for AI-powered full-stack observability and anomaly detection
Build and manage the ELK Stack (Elasticsearch, Logstash, Kibana) for enterprise log management at scale
Instrument distributed systems with distributed tracing — understanding trace context propagation, sampling strategies, and tail-based sampling
Build comprehensive AWS CloudWatch observability — custom metrics, Container Insights, X-Ray, Synthetics, and cross-account monitoring
Implement AIOps using ML-based anomaly detection, intelligent alerting, and AI-assisted root cause analysis
Design observability cost governance strategies — cardinality reduction, log filtering, sampling, and telemetry pipeline optimisation
Build everything as code — dashboards, alerts, SLOs, and monitoring configurations defined in Git and deployed via CI/CD
Developer to DevOps
The Developer to DevOps pathway is built for one specific person: the software developer, frontend, backend, or fullstack who wants to evolve their career into DevOps engineering. You already write code every day. You already understand how applications work from the inside. Now it is time to learn how to ship that code faster, more reliably, and with full ownership of the infrastructure it runs on.
This pathway leverages your existing developer skills as a superpower. While others are learning to code from scratch, you will be applying your programming knowledge to infrastructure automation, pipeline engineering, and cloud architecture from week one. The result is a dramatically shorter path to job-ready DevOps skills and a unique professional profile that most hiring managers actively seek out.
Over 12 weeks, you will transform from someone who hands code over the wall to someone who owns the entire delivery pipeline from git commit all the way to a running, monitored production system. Every module is built around the mindset shift that makes great DevOps engineers: from 'it works on my machine' to 'it runs reliably in production at scale.
Learning Outcomes
By the end of this programme, participants will be able to:
Design and implement production-grade CI/CD pipelines using GitHub Actions and Jenkins that automate every stage from commit to deployment
Containerize applications of any language and framework using Docker with optimised, production-ready Dockerfiles
Deploy and manage containerized workloads on Kubernetes — locally and on AWS EKS — with Helm chart-based deployments
Provision cloud infrastructure using Terraform with modular, environment-parameterised configurations
Implement GitOps workflows using ArgoCD to deliver continuous deployments driven entirely from Git
Apply DevSecOps practices — integrating SAST, SCA, container scanning, and secrets management into every pipeline
Instrument applications and infrastructure with Prometheus, Grafana, Loki, and distributed tracing using OpenTelemetry
Configure and manage AWS services required for modern application deployment: ECS, EKS, Lambda, RDS, S3, VPC, IAM, Route 53
Write Python and Bash automation scripts that solve real operational problems
Build a GitHub portfolio of production-grade DevOps projects that demonstrate job-ready skills to employers
Speak confidently in DevOps interviews — explaining technical decisions, trade-offs, and architecture choices
Cloud & Infrastructure
The Cloud & Infrastructure pathway is a rigorous 12-week specialisation designed to produce highly competent cloud engineers who can design, build, secure, and optimise cloud infrastructure at scale. This is not an introductory course — it is a deep-dive into the architectural and operational disciplines that companies hiring cloud engineers actually test for.
Participants will master AWS as the primary cloud platform (the world's #1 by market share), while gaining working knowledge of Microsoft Azure and Google Cloud Platform — the multi-cloud fluency that separates mid-level from senior cloud engineers. The curriculum is built around the skills and tools that appear most frequently in cloud engineer job descriptions globally: advanced networking, infrastructure automation, cloud security, serverless architectures, managed databases, cost optimisation, and cloud-native application delivery.
Every module is grounded in real-world architecture scenarios. Participants will not just deploy resources — they will design systems, justify technical decisions, optimise costs, and build portfolio-ready infrastructure that they can walk through confidently in interviews.
Learning Outcomes
By the end of this programme, participants will be able to:
Design and deploy production-grade, highly available architectures on AWS using industry best practices
Build and manage advanced AWS networking including multi-VPC designs, Transit Gateway, VPN, and Direct Connect concepts
Architect multi-cloud solutions spanning Hetzner, AWS, Microsoft Azure, and Google Cloud Platform (Should be able to use with cloud provider)
Provision and manage all cloud infrastructure using Terraform at enterprise scale with modules, workspaces, and remote state
Design and implement serverless architectures using AWS Lambda, API Gateway, SQS, SNS, and EventBridge
Configure and optimise managed database services including RDS, Aurora, DynamoDB, and ElastiCache (backup restore, migrate)
Implement comprehensive cloud security using IAM, SCPs, KMS, Secrets Manager, GuardDuty, Security Hub, and WAF
Build fault-tolerant, globally distributed systems with Route 53, CloudFront CDN, and multi-region failover
Architect cost-optimised cloud environments using Savings Plans, Spot Instances, right-sizing, and AWS Cost Explorer
Implement cloud-native monitoring and observability using CloudWatch, CloudTrail, AWS Config, and third-party tools
Design hybrid cloud architectures connecting on-premise data centres to cloud environments
Prepare confidently for the AWS Certified Solutions Architect Associate examination
DevOps Foundations
The DevOps Foundations pathway is a comprehensive 12-week program designed to take participants from zero to job-ready in the core disciplines of modern DevOps engineering. This pathway serves as the bedrock for anyone pursuing a career in DevOps, cloud engineering, or platform operations.
Participants will build deep practical knowledge across the full DevOps toolchain — from understanding DevOps culture and cloud computing fundamentals through to containerization, CI/CD pipelines, infrastructure automation, and configuration management. Every module is built around real-world use cases and hands-on projects that mirror what engineers do on the job daily.
The program is structured as two 3-hour sessions per week over 12 weeks. Each session combines concept teaching with practical labs, ensuring participants leave every class with something working and deployable.
Learning Outcomes
By the end of this course, participants will be able to:
Explain the principles, culture, and practices of DevOps and how they improve software delivery
Set up and manage AWS infrastructure including EC2, VPC, S3, IAM, and load balancers
Write Python scripts to automate infrastructure and operational tasks
Manage Linux servers, perform system administration, and write Bash scripts for automation
Provision and manage cloud infrastructure using Terraform (Infrastructure as Code)
Manage source code and collaborate effectively using Git and GitHub
Containerize applications using Docker and manage multi-container environments with Docker Compose
Orchestrate containerized workloads using Kubernetes
Build and manage CI/CD pipelines using Jenkins to automate build, test, and deployment processes
Manage server configuration at scale using Ansible