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AWS, Azure and Google Cloud AI Cloud AI

AWS, Azure and Google Cloud AI

Learn to build, deploy, and manage AI solutions using AWS, Microsoft Azure, and Google Cloud platforms.

This course introduces cloud-based artificial intelligence and machine learning services across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. Students explore cloud AI platforms, generative AI services, machine learning workflows, data storage, model deployment, security, and cost management. Through practical labs and projects, learners develop skills to build, integrate, and deploy scalable AI applications across leading cloud environments.

Duration
4 Months
Level
Beginner
Format
Online
Language
English, Hindi
Time commitment
8–10 hours per week
Course code
CLOUD-AI-101

Who this course is for

Students, graduates, software developers, data analysts, cloud engineers, DevOps professionals, IT administrators, and aspiring AI engineers interested in cloud-based AI and machine learning solutions.

What you will learn

  • - Understand cloud computing and AI service fundamentals.
  • - Compare AWS, Azure, and Google Cloud AI capabilities.
  • - Configure basic cloud resources and access permissions.
  • - Explore managed machine learning platforms on all three clouds.
  • - Integrate generative AI models into applications.
  • - Build and test AI-powered application prototypes.
  • - Prepare datasets for cloud-based machine learning workflows.
  • - Deploy AI models and inference endpoints.
  • - Integrate cloud storage, databases, and AI services.
  • - Monitor application performance, logs, and resource usage.
  • - Apply cloud security and responsible AI practices.
  • - Estimate cloud costs and optimize resource utilization.
  • - Design scalable architectures for cloud-based AI applications.

Course curriculum

Module 1: Cloud Computing and AI Fundamentals Module 2: Amazon Web Services AI Module 3: Microsoft Azure AI Module 4: Google Cloud AI Module 5: Data Engineering for Cloud AI Module 6: Machine Learning on Cloud Platforms Module 7: Generative AI and LLM Applications Module 8: Cloud AI Integration Module 9: Security, MLOps, and Cost Optimization Module 10: Capstone Project

Prerequisites

- Basic computer and internet knowledge. - Basic programming knowledge is recommended. - Familiarity with Python is helpful. - Basic understanding of cloud computing is beneficial. - A laptop or desktop with internet access. - Cloud accounts for practical exercises; some services may incur usage charges.

Projects and practical work

- Project 1: Deploy a machine learning model using Amazon SageMaker. - Project 2: Build a generative AI application using Amazon Bedrock. - Project 3: Create an AI-powered application using Azure AI services. - Project 4: Develop a Gemini-powered prototype using Google Cloud Vertex AI. - Project 5: Build a cloud-based document analysis and question-answering solution. - Final Project: Design and deploy a cloud AI application on one selected platform, integrating a model, cloud storage, secure access, monitoring, and cost controls.

Certificate

Certificate of Course Completion Students who successfully complete the course modules, practical assignments, assessments, and capstone project will receive an institute-issued AWS, Azure and Google Cloud AI Course Completion Certificate.