AI Cloud Engineer for Beginners and Experts to Lead in the Age of AI
Learn through live instructor-led sessions where you set up cloud environments, deploy real AI models, and build production-ready infrastructure with hands-on expert guidance.
Perfect for anyone – students, IT professionals, developers, and business owners – who wants to understand and use cloud platforms to host and scale AI-powered systems.
120 hours of learning covering cloud fundamentals, compute, storage, networking, AI deployment, containers, security, CI/CD, and cost optimisation
Get a certificate to show your skills in architect, deploy, and manage AI workloads on leading cloud platforms like AWS, Azure, and GCP.
Transform Ideas into AI Solutions with AI Cloud Engineer
Module 1
- Understand IaaS, PaaS, SaaS and when to use each
- Compare AWS, Azure, GCP across pricing, services, regions
- Set up cloud accounts, billing alerts, and free-tier practices
Module 2
- Launch and configure EC2 and Azure VMs for workloads
- Implement auto-scaling and load balancers for traffic spikes
- Deploy serverless functions with Lambda, triggers, and API Gateway
Module 3
- Store and manage files using S3 and Azure Blob Storage
- Set up relational and NoSQL databases for diverse data needs
- Design backups, enable recovery, and implement multi-region replication
Module 4
- Design VPCs with subnets, routing, gateways, and NAT
- Configure security groups, ACLs, and IP schemes securely
- Implement IAM roles, policies, and encryption best practices
Module 5
- Use SageMaker and Azure ML to deploy scalable AI models
- Configure inference pipelines, auto-scaling, and real-time performance monitoring
- Integrate model APIs into apps, chatbots, and workflows
Module 6
- Build Docker images, run containers, manage registries, understand lifecycle
- Deploy and scale apps using Kubernetes pods, deployments, services
- Automate workflows using GitHub Actions and Azure DevOps pipelines
Tools You Master
Meet Your Mentors
Ganta Srinath Reddy
Founder & CEO - The AI SCHOOL
Gopi Krishna Lakkepuram
Founder & CEO - Hyperleap AI
T M Praneeth Naidu
Founder & CTO - Cognisys AI
Key Outcomes
Deploy and manage cloud infrastructure across AWS, Azure, and GCP
Host and scale AI models using managed services and containers
Secure environments using IAM, VPCs, encryption, and compliance frameworks
Automate deployments with CI/CD and monitor systems using observability tools
Still wondering if this AI Cloud Engineer for you?
If you want to build, deploy, and scale AI systems in the cloud without needing years of DevOps experience, this course is built for you.
- Students
- Technologists
- DevOps
- Data Scientists
- Business Owners
- AI Enthusiasts
- Career Switchers
- Operators
Get Certified by Leaders in AI Cloud and DevOps Engineering
Frequently Asked Questions (FAQs)
No prior experience is needed. The course starts from the basics and builds up step-by-step so you can follow along even if you have never used a cloud platform before.
You will get hands-on exposure to AWS, Microsoft Azure, and Google Cloud Platform through guided labs covering compute, storage, networking, and AI deployment on each.
Yes. You will deploy trained AI models as live endpoints using SageMaker and Azure ML so real applications can call them via API not just theory, but production-ready deployments
This course focuses specifically on AI workloads in the cloud deploying models, building pipelines, and integrating cloud services around AI use cases. It is practical and role-specific, not just exam-focused.
Anyone who wants to go from cloud beginner to someone who can independently deploy AI systems in production students, professionals, developers, and entrepreneurs all benefit equally from this course.
Start learning today!
- Inclusions
- 5 Hours
- Get Certified by the Best in AI Skilling and Teacher Training
- Mentors: Tech Startup Founders