Build with AI
AI on the Cloud: Build and Deploy AI Applications
Using a chatbot is easy. Running an AI application that a business can rely on is cloud engineering: an API, access control, logs, cost limits and a person who approves what the AI sends. This course teaches both halves.
Why this course
Why AI belongs on a cloud course
Companies are moving from trying AI tools to running AI inside their own systems. The engineers they need can call a model from code, connect it to the company's documents, deploy it securely and keep the bill under control.
Simply2Cloud's affiliated companies build exactly this kind of system, so the projects reflect real requirements rather than demos.
- Python from the basics needed for AI work.
- Calling AI models through APIs, and prompt design that holds up in code.
- Answers grounded in your own documents (retrieval), with sources shown.
- Serverless deployment on AWS, with an Azure comparison.
- Security, logging, cost limits and human approval built in.
Graduates and developers who want to build AI features, and cloud learners who want to add AI deployment to their skills.
Basic computer use. Python is taught from the start; any earlier programming helps.
There is no single industry exam for this course. Learners who want a credential can follow with AWS or Azure AI certifications; exam fees are separate.
AI application developer (junior) · Cloud engineer with AI deployment skills · Automation engineer
How the course runs
The plan, in the order we teach it.
- Months 1–2
Python for AI work: data types, functions, files, JSON, calling web APIs. AI fundamentals and how language models behave.
- Months 3–4
Working with AI model APIs, prompt design, structured outputs, retrieval from your own documents and testing answers.
- Months 5–6
Deploying on AWS (API Gateway, Lambda, storage, secrets, logs), cost limits and monitoring, an Azure comparison, then the final project.
Your project
A support assistant that cites its sources
An API that answers questions from a set of company documents, shows which document each answer came from, refuses when it does not know, and waits for a person to approve anything it would send.
From course project to real projectsWhat you hand in
- A deployed API with authentication
- Tests for unsupported questions and untrusted instructions
- Logs, a cost limit and an alarm
- A demo and a short design note
Syllabus
Curriculum designed by working engineers.
Tools covered
UKIQ certificate. Professional batches include a UKIQ certificate alongside the project you can show in an interview.
01Foundations
- Python for AI work
- Calling web APIs and handling JSON
- What language models can and cannot do
- AI safety and data protection basics
02Building with AI models
- Model APIs and prompt design
- Structured outputs for use in code
- Retrieval: answering from your own documents
- Testing answers and handling failure
03Deploying in the cloud
- Serverless APIs on AWS
- Secrets, access control and logging
- Cost limits and monitoring
- Azure AI services compared
- Human approval before consequential actions
FAQ
Questions before you enrol.
Do I need to know programming?
No. Python is taught in the first two months, focused on what AI applications need.
Which AI models do you use?
Current models from major providers, accessed through their APIs. Model choices are updated as the field changes; the engineering habits stay the same.
What does the fee include?
₹45,000 covers six months of classes and labs, the final project and placement assistance. Cloud and model usage in labs is kept within free tiers or small budgets that we show you how to set.