Training that builds production-ready engineers.

Instructor-led programmes in DevOps, Docker and Kubernetes, cloud, Generative AI and MLOps, for corporate teams and individual professionals. Delivered online live or on-site, with hands-on labs, capstone projects and certification preparation.

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  • Hands-on labs
  • Capstone projects
  • Certification preparation
  • Certificate of completion

Delivery formats

Training that fits how your team works.

  • Corporate team programmes

    Private cohorts for your engineering teams, tailored to your stack and goals.

  • Individual & professional

    Focused programmes for engineers growing into DevOps, cloud and AI roles.

  • Online live

    Instructor-led, interactive sessions with live labs, wherever your team is.

  • On-site

    Intensive, in-person training delivered at your office.

Programme catalogue

9 programmes. Module-by-module curricula.

Every programme can run as published or be tailored to your team. Open a curriculum to see exactly what is covered.

DevOps

DevOps Engineering

A complete, hands-on programme from Linux fundamentals to secure, automated delivery on Kubernetes.

Level
Beginner to intermediate
Duration
10 days · 80 hrs
Format
Corporate · Individual · Online live · On-site

  1. 01
    Linux and shell essentialsFilesystem, processes, permissions, systemd, networking basics and Bash scripting.
  2. 02
    Git and collaboration workflowsBranching strategies, merge requests and code review, trunk-based development, semantic versioning.
  3. 03
    Continuous integration with Jenkins and GitLab CIPipelines as code, build and test stages, artefacts, runners and agents, caching.
  4. 04
    Containers with DockerDockerfiles, multi-stage builds, registries and image scanning.
  5. 05
    Kubernetes for delivery teamsDeployments, Services, ConfigMaps and Secrets, rolling updates and rollbacks.
  6. 06
    Infrastructure as Code with TerraformProviders, state, modules, and plan and apply inside pipelines.
  7. 07
    Configuration management with AnsibleInventories, playbooks, roles and idempotent automation.
  8. 08
    Monitoring with Prometheus and GrafanaMetrics, exporters, PromQL, dashboards and alerting rules.
  9. 09
    DevSecOpsSecrets management, SAST, dependency and container scanning, policy as code.
  10. 10
    Capstone: commit to productionAn end-to-end pipeline that tests, scans, provisions and deploys a service to Kubernetes, with monitoring.
You’ll be able to
  • Build CI/CD pipelines that test, scan and deploy automatically
  • Containerise applications and run them on Kubernetes
  • Provision and configure infrastructure with Terraform and Ansible
  • Monitor services and alert on what matters
Prerequisites

Basic command-line familiarity and some experience with any programming or scripting language.

DevOps

Docker & Kubernetes

From container fundamentals to running, scaling and troubleshooting production workloads on Kubernetes.

Level
Intermediate
Duration
5 days · 40 hrs
Format
Corporate · Individual · Online live · On-site

Certification preparationCKA and CKAD exam preparation

  1. 01
    Containers and Docker fundamentalsNamespaces and cgroups, the container lifecycle and the Docker CLI.
  2. 02
    Building production imagesDockerfile best practices, multi-stage builds, minimal and secure base images, registries.
  3. 03
    Docker ComposeMulti-container applications, networks, volumes and local development environments.
  4. 04
    Kubernetes architectureControl plane, nodes, etcd, kubelet, kubectl and declarative manifests.
  5. 05
    WorkloadsPods, Deployments, StatefulSets, DaemonSets, Jobs and CronJobs, probes and resource limits.
  6. 06
    NetworkingServices, Ingress, cluster DNS and NetworkPolicies.
  7. 07
    StorageVolumes, PersistentVolumes and claims, StorageClasses and stateful applications.
  8. 08
    Packaging with HelmCharts, values, templating, releases and rollbacks.
  9. 09
    SecurityRBAC, service accounts, Pod Security Standards and Secrets.
  10. 10
    Autoscaling and troubleshootingHPA, cluster autoscaling, logs and events, debugging failing Pods, CKA and CKAD practice scenarios.
You’ll be able to
  • Build small, secure container images
  • Deploy and expose stateless and stateful applications on Kubernetes
  • Package and release applications with Helm
  • Diagnose and fix common cluster and workload issues
Prerequisites

Comfortable with the Linux command line. Docker basics help but are not required.

Cloud

Cloud Engineering on AWS, Azure & Google Cloud

Core cloud services and architecture on the provider you choose, taught through guided labs in a live cloud account.

Level
Beginner to intermediate
Duration
5 days · 40 hrs per cloud
Format
Corporate · Individual · Online live · On-site

Certification preparationAWS Solutions Architect – Associate, Azure Administrator (AZ-104) and Google Cloud Associate Cloud Engineer preparation

  1. 01
    Cloud foundationsShared responsibility, regions and zones, accounts, subscriptions and projects.
  2. 02
    Identity and accessIAM users, roles and policies, Microsoft Entra ID and Azure RBAC, least privilege.
  3. 03
    NetworkingVPCs and VNets, subnets, routing, load balancers, DNS and private connectivity.
  4. 04
    ComputeVirtual machines, autoscaling, and managed Kubernetes with EKS, AKS and GKE.
  5. 05
    StorageObject, block and file storage, lifecycle policies and backups.
  6. 06
    DatabasesManaged relational and NoSQL databases, high availability and read replicas.
  7. 07
    ServerlessAWS Lambda, Azure Functions, Cloud Run and Cloud Functions, event-driven design.
  8. 08
    Monitoring and costCloudWatch, Azure Monitor and Cloud Monitoring, budgets, tagging and cost optimisation.
  9. 09
    Well-architected designReliability, security, performance and cost pillars, and running an architecture review.
  10. 10
    Certification preparationExam domains, practice questions and scenario walkthroughs.
You’ll be able to
  • Design secure, highly available cloud architectures
  • Configure identity, networking and access correctly
  • Choose the right compute, storage and database services
  • Keep cloud spend visible and under control
Prerequisites

Basic IT and networking knowledge. No prior cloud experience needed.

DevOpsCloud

Infrastructure as Code with Terraform & Ansible

Provision and configure repeatable infrastructure, reviewed and applied through pipelines.

Level
Intermediate
Duration
4 days · 32 hrs
Format
Corporate · Individual · Online live · On-site

Certification preparationHashiCorp Terraform Associate preparation

  1. 01
    IaC principlesDeclarative and imperative approaches, immutable infrastructure and drift.
  2. 02
    Terraform fundamentalsHCL, providers, resources, variables and outputs.
  3. 03
    State managementState backends, locking, workspaces and importing existing resources.
  4. 04
    Reusable modulesWriting, versioning and publishing modules.
  5. 05
    Terraform in CI/CDPlan reviews, automated apply and policy checks.
  6. 06
    Ansible fundamentalsInventories, ad-hoc commands and playbooks.
  7. 07
    Ansible at scaleRoles, variables, Jinja2 templates and Ansible Vault for secrets.
  8. 08
    CapstoneProvision a cloud environment with Terraform and configure it with Ansible.
You’ll be able to
  • Provision repeatable environments across clouds
  • Structure Terraform code with modules and shared state
  • Automate server configuration with Ansible
  • Run infrastructure changes through reviewed pipelines
Prerequisites

Linux command line and basic cloud concepts.

AI

Generative AI & LLM Applications

Build, evaluate and deploy LLM applications grounded in your own data, from prompts to RAG and agents.

Level
Intermediate
Duration
4 days · 32 hrs
Format
Corporate · Individual · Online live · On-site

  1. 01
    LLM fundamentalsTokens, context windows, model families, and choosing open or hosted models.
  2. 02
    Prompt engineeringClear instructions, few-shot examples, structured output and reusable prompt patterns.
  3. 03
    Embeddings and semantic searchEmbedding models, similarity search and chunking strategies.
  4. 04
    Retrieval-augmented generation (RAG)Ingestion pipelines, retrieval, re-ranking, grounding and citations.
  5. 05
    Vector databasesIndexing, metadata filtering and choosing a vector store.
  6. 06
    Agents and tool callingFunction calling, multi-step workflows and memory.
  7. 07
    EvaluationTest sets, automated and human evaluation, and regression testing for prompts.
  8. 08
    Guardrails and safetyPrompt injection, PII handling and content filtering.
  9. 09
    Deploying LLM applicationsAPIs, streaming, caching, and cost and latency monitoring.
  10. 10
    CapstoneBuild and deploy a RAG assistant over your own documents.
You’ll be able to
  • Build RAG applications grounded in your own data
  • Design reliable prompts and structured outputs
  • Add tool calling and agent workflows
  • Evaluate, secure and deploy LLM applications
Prerequisites

Working knowledge of Python.

AI

AI Integration for Business & Developers

Put AI to work in your products and workflows, safely, with a business track and a developer track.

Level
All levels
Duration
2 days · 16 hrs
Format
Corporate · Individual · Online live · On-site

  1. 01
    The AI landscapeWhat LLMs can and cannot do, and finding use cases with clear value.
  2. 02
    Integrating AI APIsAuthentication, requests, streaming, error handling and retries.
  3. 03
    AI features in your productsChat, summarisation, classification and data extraction.
  4. 04
    Workflow automationConnecting AI to email, documents and internal tools.
  5. 05
    Copilots for teamsCoding and knowledge assistants, and adoption practices that stick.
  6. 06
    Data privacy and securityData handling, access control and vendor review.
  7. 07
    Responsible AIBias, transparency, human oversight and governance.
  8. 08
    From pilot to rolloutMeasuring value, controlling cost and managing change.
You’ll be able to
  • Identify AI use cases with clear business value
  • Integrate AI APIs into products and workflows
  • Use copilots productively and safely
  • Put privacy and governance guardrails in place
Prerequisites

None for the business track. Basic programming for the developer track.

AI

MLOps

Take machine learning models from experiment to reliable, monitored production systems.

Level
Intermediate to advanced
Duration
4 days · 32 hrs
Format
Corporate · Individual · Online live · On-site

  1. 01
    The ML lifecycleFrom data to deployment, team roles and MLOps maturity levels.
  2. 02
    Experiment tracking with MLflowRuns, metrics, artefacts and the model registry.
  3. 03
    Data and feature managementData versioning, feature stores and training-serving consistency.
  4. 04
    ML pipelinesOrchestrated, reproducible training pipelines and CI/CD for ML.
  5. 05
    Model packagingContainerising models, model formats and inference APIs.
  6. 06
    Model serving on KubernetesOnline and batch inference, autoscaling and canary releases.
  7. 07
    Monitoring and driftData and concept drift, performance monitoring, alerting and retraining triggers.
  8. 08
    CapstoneAn automated pipeline from training to a monitored deployment.
You’ll be able to
  • Track experiments and manage models in a registry
  • Build reproducible training pipelines
  • Serve models on Kubernetes with safe rollouts
  • Detect drift and trigger retraining
Prerequisites

Python and basic machine learning. Docker familiarity helps.

DevOps

Platform Engineering & SRE

Run reliable platforms with clear SLOs, deep observability, calm incident response and GitOps delivery.

Level
Advanced
Duration
4 days · 32 hrs
Format
Corporate · Individual · Online live · On-site

  1. 01
    Platform engineeringInternal developer platforms, golden paths and self-service.
  2. 02
    SLIs, SLOs and error budgetsDefining reliability targets that reflect user experience.
  3. 03
    ObservabilityMetrics, logs and traces with Prometheus, Grafana and OpenTelemetry.
  4. 04
    AlertingSymptom-based alerts, reducing noise and on-call practices.
  5. 05
    Incident responseRoles, runbooks, communication and blameless post-incident reviews.
  6. 06
    GitOps with Argo CDDeclarative delivery, sync strategies and progressive delivery.
  7. 07
    Capacity and resilienceLoad testing, autoscaling and chaos experiments.
  8. 08
    CapstoneDefine SLOs, dashboards and a GitOps delivery flow for a sample service.
You’ll be able to
  • Define SLOs and error budgets that guide decisions
  • Instrument services for metrics, logs and traces
  • Run incidents and reviews with confidence
  • Deliver to Kubernetes with GitOps and Argo CD
Prerequisites

Experience with Kubernetes and CI/CD.

DevOpsAI

Python for DevOps & AI Automation

Practical Python for engineers who want to automate infrastructure, cloud and AI tasks.

Level
Beginner
Duration
3 days · 24 hrs
Format
Corporate · Individual · Online live · On-site

  1. 01
    Python essentialsData types, functions, modules and virtual environments.
  2. 02
    Files and dataJSON, YAML, CSV and regular expressions.
  3. 03
    Automation scriptssubprocess, scheduling and command-line tools with argparse.
  4. 04
    APIs and HTTPrequests, REST APIs and authentication.
  5. 05
    Cloud automationThe AWS SDK (boto3) and the Azure and Google Cloud SDKs.
  6. 06
    Calling AI models from PythonLLM APIs, embeddings and simple AI-powered automation.
  7. 07
    Testing and packagingpytest, linting, and packaging tools to share with your team.
You’ll be able to
  • Write clean, tested automation scripts
  • Automate cloud resources through SDKs
  • Integrate REST and AI APIs into your tools
  • Package and share internal tooling
Prerequisites

None. Ideal as a first step before DevOps or GenAI programmes.

Learning paths

Structured paths to a new role.

Combine programmes in sequence to build skills for a complete role, at the pace that suits your team.

DevOps Engineer

5 programmes · 208 hrs

Automate delivery end to end, from code to reliable production.

  1. 1Python for DevOps & AI Automation
  2. 2DevOps Engineering
  3. 3Docker & Kubernetes
  4. 4Infrastructure as Code with Terraform & Ansible
  5. 5Platform Engineering & SRE

Cloud Engineer

4 programmes · 144 hrs

Design, build and run secure infrastructure on the major clouds.

  1. 1Cloud Engineering on AWS, Azure & Google Cloud
  2. 2Infrastructure as Code with Terraform & Ansible
  3. 3Docker & Kubernetes
  4. 4Platform Engineering & SRE

GenAI Engineer

4 programmes · 104 hrs

Ship LLM applications and the platforms that keep them reliable.

  1. 1Python for DevOps & AI Automation
  2. 2Generative AI & LLM Applications
  3. 3AI Integration for Business & Developers
  4. 4MLOps

How our training works

From assessment to certificate.

  1. 01

    Skills assessment

    We assess current skills and goals so every session starts at the right level.

  2. 02

    Tailored curriculum

    Modules, depth and examples matched to your stack, tools and timeline.

  3. 03

    Hands-on labs

    Guided labs on real tools and cloud environments, not slides alone.

  4. 04

    Capstone project

    Participants apply everything to an end-to-end, production-style project.

  5. 05

    Certificate of completion

    Each participant who completes the programme receives a certificate of completion.

Train your team

Plan a programme for your team.

Tell us about your team, goals and timeline. We will recommend the right programmes and a tailored curriculum.

Enquire about training