AI Cloud Engineer (For All)

"Turn AI Ideas into Cloud-Powered Solutions. "

The AI SCHOOL and STTAR invite you to join ONE WEEK Online Certificate program on AI Cloud Engineer

AI Cloud Engineer for Beginners and Experts to Lead in the Age of AI

Online Mode

Skill Level

Duration

Certificate

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

Cloud Computing Fundamentals & Platform Overview
Module 1

Understand core cloud concepts including IaaS, PaaS, and SaaS and when to use each model.
Compare AWS, Azure, and GCP across pricing, services, global regions, and availability zones.
Set up cloud accounts, navigate consoles, configure billing alerts, and apply free-tier best practices.

Compute Services & Scalable Infrastructure
Module 2

Launch and configure EC2 instances and Azure Virtual Machines with the right instance types for your workload.
Implement auto-scaling policies and load balancers to handle traffic spikes without downtime.
Deploy serverless functions using AWS Lambda and Azure Functions with event-driven triggers and API Gateway integration.

Cloud Storage & Database Solutions
Module 3

Store and manage files at scale using S3 and Azure Blob Storage with lifecycle policies and access controls.
Set up relational databases (RDS, Azure SQL) and NoSQL options (DynamoDB, Cosmos DB) for different data needs.
Design backup strategies, enable point-in-time recovery, and implement multi-region data replication

Cloud Networking & Security Fundamentals
Module 4

Design Virtual Private Clouds (VPCs) with subnets, route tables, internet gateways, and NAT configurations.
Configure security groups, network ACLs, and IP addressing schemes to control traffic flow securely.
Implement IAM roles, policies, and encryption standards to meet compliance and cloud security best practices.

Deploying AI Models on Cloud Platforms
Module 5

Use SageMaker and Azure Machine Learning to host, version, and serve trained AI models as live endpoints.
Configure inference pipelines, auto-scaling for model serving, and real-time monitoring of model performance.
Integrate deployed model APIs into web apps, chatbots, and business workflows using REST and SDK calls.

Containerisation, Kubernetes & CI/CD Pipelines
Module 6

Set up CloudWatch and Azure Monitor dashboards, log streams, and automated alerts for your cloud resources.
Analyse logs to detect anomalies, trace performance bottlenecks, and ensure uptime for AI workloads.
Reduce spend using resource tagging, reserved and spot instances, right-sizing, and cloud cost monitoring tools.

Tools You Master

Meet Your Mentors

Key Outcomes

Deploy and manage cloud infrastructure across AWS, Azure, and GCP with confidence.
Host and scale AI models in production using managed ML services and containerised workloads.
Secure cloud environments using IAM, VPCs, encryption, and compliance frameworks.
Automate deployments with CI/CD pipelines and monitor systems using cloud-native 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.

Get Certified by the Best in AI Skilling and Teacher Training

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Frequently Asked Questions (FAQs)

1. Do I need prior cloud or coding experience to join this course?

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.

2. Which cloud platforms will I be working with?

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.

3. Will I actually deploy a real AI model during the course?

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

4. What is the difference between this course and a general cloud certification prep?

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.

5. Who is this course best suited for?

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.

India's only school where startup Leaders teach AI skills.