AWS Cloud Engineering & AI

AWS Cloud Engineering & AI is a 42-week, full-time, 100% online course that takes you from the fundamentals to a job-ready cloud engineer. You'll build expertise across AWS, Linux, Python, Docker, Kubernetes, Terraform, CI/CD and applied AI, then design and deploy a large-scale final project. Delivered by an official AWS Partner through live, instructor-led sessions and hands-on labs, it includes 5 industry-recognised certifications and 12 months of career support.

• Before You Learn / Who This Course Is For

  • Beginners and for people with a tech background who want to specialise in cloud engineering, DevOps and AI.

  • Suits career changers moving into tech, IT and tech professionals levelling up, graduates and emerging talent, and business or operations professionals moving into a technical cloud role.

  • No prior cloud experience is required.

• What to Expect

  • Expect live, instructor-led classes Monday to Friday, hands-on labs in real cloud environments, and collaborative projects that build a job-ready portfolio.

  • You'll finish with a large-scale final project and receive a MacBook Air, plus 12 months of career support.

  • Eligible learners can train for €0 with a Bildungsgutschein.

• What You'll Achieve
  • Graduates will be prepared for five industry-recognised certifications: Linux Essentials, AWS Certified Cloud Practitioner and AWS Certified AI Practitioner (completed during the course), plus PCEP and AWS Certified Solutions Architect – Associate (after graduation).

  • By the end of the course, students will be able to:

     

    • Design, build and operate cloud infrastructure on AWS, using core services like EC2, S3, VPC, IAM, Lambda, RDS and CloudFormation

    • Administer Linux and work confidently on the command line

    • Write Python for cloud scripting and automation, including AI-assisted coding with Claude Code

    • Store, manage and query data using SQL and AWS database services

    • Containerise applications with Docker and orchestrate them with Kubernetes

    • Provision and manage infrastructure as code with Terraform

    • Build CI/CD pipelines with GitHub Actions and automate delivery

    • Set up load balancing, DNS and observability, and script operational tasks

    • Apply AI and machine learning services on AWS

    • Plan, build and present a large-scale, cloud-native final project for a job-ready portfolio