Applied Cloud Computing (AWS, MS Azure & GCP)

Duration: 12 months

Become a cloud professional with the ability to work on platform-agnostic and cloud-based solutions for organizations.

Become proficient in AWS and Microsoft Azure with an option to learn Google Cloud.

Be able to work with Containers, Microservices, DevOps, Cloud Design Patterns, and Openstack.

Get a better understanding of Cloud Financial, Adoption & Migration Patterns and learn to make trade-off decisions for your customers.

Understand Big Data and Architectural Styles to realize the value of data insights for your organization and customers.

Get access to an optional Developer Track to hone your skills (only for developers with work experience).



  1. Hands-on Labs & Projects
    Solve and build more than 15 use cases through labs and projects across various cloud platforms.
  2. Capstone Project
    Specialize in cloud concepts & domains by working on a set of use cases inspired by real-life industry problems.
  3. Lecture Notes
    Apart from the training, demos, and mentoring, you’ll also, access course notes for a quick overview of concepts before your interviews.
  4. Access to Learning Beyond Graduation
    Continue learning even after completion of the program.
  5. Comprehensive Curriculum
    The program covers a diverse yet relevant set of concepts and technologies that prepare you well beyond just industry certifications.
  6. Get Certified
    Certificate from our partner Universities.
  7. Resume Building and Interview Preparation
    We help you build your resume to highlight your skills and your previous professional experience. You’ll also learn to crack interviews with our interview preparation sessions.
  8. Career Guidance
    Get access to career mentoring from industry experts who’ve transitioned to important roles in the industry.

Course Outline for Applied Cloud computing (AWS, MS Azure & GCP)


  1. Cloud Primer (Preparatory Work Course)
    Basic Python for Cloud

– Basic Linux

  1. Cloud Foundations
    Introduction to Virtualization

(VM’s and Containers)

– Service Delivery and Deployment Models

– Cloud Attributes and Services Taxonomy

– Introduction to Infrastructure Automation

– Key Aspects of IaaS, PaaS and SaaS



(1 Assignment, 1 Project)

  1. Cloud Computing on AWS
    Compute, Load Balancing, Autoscaling

– Storage, Replication and Life Cycle

– Management

– AWS Organization and Identity

– Networking and Data migration

  1. Managed Services on AWS

– Databases (RDS, DynamoDB)

– Web Application Firewall

– SNS, SQS, Cloudwatch

– Athena, Quicksight and Kinesis

– Serverless (Lambda)

– AWS Cognitive Services (Rekognition, Comprehend & Polly)

  1. Containers and AWS DevOps
    (1 Assignment, 1 Project)

– Docker

– Elastic Container Service (ECS)

– Deployment Pipeline (AWS Code Commit, AWS Code Deploy, AWS Code Pipeline)

– CloudFormation

– Terraform

  1. Enterpise Cloud Solutions

– Data Architecture and Serverless approach

– Setting up a Cloud-Based Development Environment

– Data Streaming and Data Analytics on cloud

– Setting up Kinesis Data Stream

– Platform as a service – Elastic Beanstalk

– Step Functions

– Elasticsearch

  1. Solution Architect

– Networking

– Managed Services Overview

– Security

  1. Application Developer

– Architecture & DevOps

– Platforms

– Data & Databases

– Web, Mobile & Gaming


  1. Azure Infrastructure
    (1 Assignment, 2 Projects)

– Introduction to Azure & its services

– Azure Virtual Machines (Networking Components, Configuring High Availability,          Scale sets, Autoscaling, etc.)

– Storage (Blob Storage, Azure Files)

– Virtual Networking (Networking Options, VNet Peering, VNet to VNet setup)

– Active Directory (Basics on Azure AD & AD connect)

– Azure Resource Manager (Building ARM template, Powershell, CLI, Cloud shell usage)

  1. Azure Solution Architect

– Load Balancing (Azure Load Balancer, Application Gateway, WAF, Azure Traffic Manager)

– Network Security (Network Security Groups, Azure Bastion, Firewall)

– Security & Governance Solutions

  1. Azure Solutions for Apps

– Azure App Services (Web Apps, Azure Functions, Logic Apps)

– API Management

– Container-based Applications

– Messaging & Event-Based Services (Service Bus, Event Hub, Event Grid)

– Azure DevOps

– Monitoring (Monitoring options, Metrics, Service Health, Activity Log, Alerts, Configuring Log Analytics and Diagnostic logging, Configuring App Insights)

  1. Azure Data Platforms

– Cosmos DB (Introduction, Working with Core SQL API, Global distribution (replicas), Throughput, Consistency, Partitioning, Connect via Azure function/VM2)

– SQL Database (Database options & models)

– Data Services Intro (Azure Data Factory, Synapse, Databricks, Stream Analytics)

  1. Azure ML Services

– Azure ML Services

– AI Services Oered on Azure

– Azure ML Studio

– Azure ML & Loading data

– Data Manipulation

– Visualizing Categorical Data

– Visualizing Numerical Data, etc.


  • Expectations and Characteristics
  • Failures, Modelling challenges, Load balancing
  • The 12-factor app
  • Architecture and Design Patterns
  • Anti Patterns
  • Event-Driven Architecture
  • Security Enforcement
  • Migrating a monolith


  • Concepts of NoSQL
  • NoSQL Databases (Cassandra and DynamoDB)
  • Partitioner, Replication, Snitch
  • Query Driven Design and Distributed Development
  • Amazon EMR, Hadoop and Hive


  • AWS Organizations
  • Security services (Trusted Advisor, Data encryption, Amazon Inspector, AWS Config, Guard Duty, Macie)
  • Identity and Access Control Application user management (AWS Cognito Cost Explore
  • Cloud Migration Strategies
  • Migration Maturity Index
  • API based integration
  • Workload Analysis


  • OpenStack Essentials


  • Regions, Resources and Services Overview
  • Google Compute Engine, Instance Groups, Load Balancing and Autoscaling
  • Storage and Networking
  • GCloud and Cloud SDK
  • Google App Engine
  • Google Kubernetes Engine


  • A group project that allows you to apply your learning to real industry use cases and add it to your portfolio for potential employers to see as a tangible body of work.


Cloud-Based File Share and Sync Solution

  • The solution can be easily scaled up to run in your Data Center or on a Public Cloud, with its servers, storage, and other components completely managed and controlled by your IT team in accordance with the company’s governance and security requirements.
  • Concepts: VPC, EC2, Security Group, Internet Gateway, NAT instance, LAMP stack, S3 and MYSQL.

Building an Automated Business Process Using Managed Services on a Public Cloud

  • In the connected world, it is imperative that the organizations are interlinked with the customers and vendors. This process has been very sluggish, manual, batch-based, and prone to failures. Such integration design has led to impaired decision-making and delays in the detection of fraudulent actions. This project created an automated, event-based real-time process using managed cloud services that do not have these limitations.
  • Concepts: Amazon S3, SNS, Athena and DynamoDB.

Building Scalable and Resilient Applications

  • Creating web applications that are both resilient and scalable is an essential part of any application architecture. A well-designed application should scale seamlessly as demand increases and decreases and be resilient enough to withstand the loss of one or more compute resources.
  • Concepts: Instance Group, Autoscaler, HTTP Load balancer, Autohealing and Google App Engine.

Scalable Geo-Distributed Event Registration App on Microsoft Azure

  • The application is a multi-tenant web application that can onboard thousands of corporates and millions of end-users. The app is globally available with automatic geo-failover, as any downtime may cost revenue loss to business as end-users will not be able to register. Email notifications are sent to end-users after successful registration with relevant event information.
  • Concepts: AppServer Instances, Azure Active Directory, Functions and CDN.

Build a Document Management System on Cloud

  • Enterprise information is primarily captured either in a structured manner or an unstructured manner. Databases have been very efficient in handling structured data and allowing the business users flexibility to access the raw data. However, when it comes to handling unstructured data storage, management will have less than the desired outcome if the system used is optimized for structured data. We created a process and a Document Management System that allows the enterprise to organize, catalogue and fetch data in an intuitive manner.
  • Concepts: Lambda, Elasticsearch, Containers, Python and Java Codes, S3 and DynamoDB.

Cassandra Setup and Masterless Concepts

  • Install multi-node Cassandra cluster, induce failure, create keyspace/table and access from the client.
  • Concepts: Cassandra and BigData.