Comprehensive Multi-Cloud Bootcamp: Mastering AWS, Azure, and GCP



  • IT professionals seeking to enhance their skills in cloud computing and expand their knowledge across multiple cloud platforms.
  • Software engineers and developers aiming to master cloud infrastructure, services, and deployment strategies.
  • System administrators and network engineers looking to gain expertise in managing cloud environments and optimizing resources.
  • Technical architects and solution architects interested in designing and implementing robust and scalable multi-cloud solutions.
  • Project managers and technology leaders responsible for overseeing cloud initiatives and driving digital transformation.
  • Graduates and aspiring professionals seeking to enter the rapidly growing field of cloud computing with a competitive edge.
  • Entrepreneurs and business owners aiming to leverage the potential of cloud technologies for their organizations’ growth and efficiency.
  • Anyone with a strong interest in cloud computing and a desire to explore the capabilities of leading cloud providers.

Total Course Duration: Approximately 8 to 10 weeks

  • 2 hours per day, 5 days a week: This schedule would amount to 10 hours of training per week.
  • With a total course duration of 8 to 10 weeks, the estimated total training hours would range from 80 to 100 hours.

It’s important to note that this is an approximate duration, it can vary depending on the depth of coverage, the pace of instruction. Additionally, the duration may also be influenced by the inclusion of additional hands-on projects, assignments, or assessments.

Job opportunities will include roles such as Cloud Architect, Cloud Engineer, Cloud Consultant, and more. These roles offer attractive remuneration packages, career growth prospects, and the chance to work on cutting-edge cloud projects. According to our research through some reliable job portals here are the salary survey figures in Indian MNCs based on available industry reports :-

  1. Cloud Architect: In Indian MNCs, the salary for a Cloud Architect can range from INR 15 lakh to INR 30 lakh per annum. Senior-level Cloud Architects with extensive experience and expertise may earn salaries upwards of INR 40 lakh per annum.
  2. Cloud Engineer: For a Cloud Engineer role in an Indian MNC, the salary can range from INR 8 lakh to INR 20 lakh per annum. Entry-level Cloud Engineers can expect salaries around INR 6 lakh to INR 10 lakh per annum, while experienced professionals can earn salaries exceeding INR 25 lakh per annum.
  3. Cloud Consultant: The salary for a Cloud Consultant in Indian MNCs can vary based on experience and expertise. On average, Cloud Consultants earn salaries ranging from INR 10 lakh to INR 20 lakh per annum.

Salaries can be even higher than 40 LPA for individuals with niche skills, and a proven track record of successfully implementing and managing multi-cloud solutions. Our aim is to empower you by providing practical learning , industry relevant skills and career support so that you can prove your value during job interviews and able to secure a good job with a competitive salary in this field.


Course Outline
Course Section 1: Introduction to Cloud Computing and Cloud Providers
  1. Overview of cloud computing and its benefits
  2. Introduction to AWS, Azure, and GCP
  3. Understanding cloud service models (IaaS, PaaS, SaaS)
  4. Exploring the cloud management consoles of all three providers
Course Section 2: AWS Fundamentals
  1. Setting up an AWS account and accessing the AWS Management Console
  2. Understanding AWS regions, availability zones, and edge locations
  3. Introduction to key AWS services (EC2, S3, RDS, IAM)
  4. Deploying a basic web application on AWS
Course Section 3: Azure Fundamentals
  1. Creating an Azure account and navigating the Azure portal
  2. Overview of Azure regions, availability zones, and data centers
  3. Introduction to core Azure services (VMs, Storage, Azure SQL Database, Azure Active Directory)
  4. Building and deploying a simple application on Azure
Course Section 4: GCP Fundamentals
  1. Creating a GCP account and exploring the GCP Console
  2. Understanding GCP regions, zones, and global infrastructure
  3. Introduction to essential GCP services (Compute Engine, Cloud Storage, Cloud SQL, IAM)
  4. Deploying a sample application on GCP
Course Section 5: Advanced AWS Topics
  1. Advanced EC2 concepts (Auto Scaling, Load Balancing, Elastic Block Store)
  2. Networking in AWS (Virtual Private Cloud, Subnets, Security Groups)
  3. Serverless computing with AWS Lambda
  4. Monitoring and managing resources with AWS CloudWatch
Course Section 6: Advanced Azure Topics
  1. Azure Virtual Machines and VM scale sets
  2. Azure networking (Virtual Network, Subnets, Network Security Groups)
  3. Azure App Services and containerization with Azure Container Instances
  4. Implementing monitoring and management with Azure Monitor
Course Section 7: Advanced GCP Topics
  1. Google Compute Engine and instance groups
  2. Networking in GCP (Virtual Private Cloud, Subnets, Firewall Rules)
  3. Deploying containers with Google Kubernetes Engine (GKE)
  4. Monitoring and logging with Stackdriver in GCP
Course Section 8: Cloud Migration and Hybrid Cloud
  1. Strategies for migrating workloads to the cloud
  2. Hybrid cloud architectures and connectivity options
  3. AWS, Azure, and GCP migration tools and services
  4. Hands-on migration project to move an application to the cloud
Course Section 9: Cloud Security and Governance
  1. Identity and Access Management (IAM) in AWS, Azure, and GCP
  2. Securing cloud resources with network security groups and firewalls
  3. Compliance and regulatory considerations
  4. Designing and implementing a secure multi-cloud architecture
Course Section 10: Hands-On Projects
    1. Multi-Cloud DevOps Pipeline:

    In this project, you will create a multi-cloud Continuous Integration/Continuous Deployment (CI/CD) pipeline using AWS CodePipeline, Azure DevOps, and GCP Cloud Build. Imagine you are working for a global e-commerce company that operates across multiple cloud platforms. Your task is to establish an automated pipeline that seamlessly deploys updates to the company’s web application across AWS, Azure, and GCP. You will configure build triggers, integrate with version control systems, and automate testing, code analysis, and deployment stages. By implementing a multi-cloud DevOps pipeline, you ensure consistent and efficient software delivery, maintaining a competitive edge in the market.

    1. Serverless Data Processing and Analytics:

    In this project, you will utilize the serverless capabilities of AWS Lambda, Azure Functions, and GCP Cloud Functions for data processing and analytics. Consider a scenario where a financial services company receives a continuous stream of financial data from various sources. Your task is to build a serverless data processing pipeline that ingests, transforms, and analyzes the data in real-time. You will leverage the serverless functions to perform data processing tasks such as data enrichment, filtering, and aggregation. Additionally, you will integrate with cloud-based data analytics services like AWS Glue, Azure Data Factory, and GCP BigQuery to extract valuable insights from the processed data.

    1. Multi-Cloud Machine Learning Infrastructure:

    In this project, you will deploy a scalable machine learning infrastructure using AWS SageMaker, Azure Machine Learning, and GCP AI Platform. Imagine you are working for a healthcare startup that aims to develop advanced machine learning models for medical diagnostics. Your goal is to create a multi-cloud infrastructure that enables the training and deployment of machine learning models across AWS, Azure, and GCP. You will leverage the machine learning services provided by each cloud platform, including data preprocessing, model training, and hyperparameter tuning. By comparing the performance and cost of the models across multiple cloud providers, you ensure optimal utilization of resources and select the best cloud platform for specific machine learning tasks.

    1. Cloud-Native Kubernetes Application:

    In this project, you will develop a cloud-native application using AWS EKS, Azure Kubernetes Service (AKS), and GCP Kubernetes Engine. Consider a scenario where a transportation logistics company wants to build a scalable and resilient application for managing their operations. Your task is to design and implement a cloud-native solution using Kubernetes across multiple cloud platforms. You will containerize application components, deploy them to the respective Kubernetes clusters, and implement features such as service discovery, load balancing, and scaling. By leveraging Kubernetes on different cloud platforms, you enable seamless application deployment, high availability, and efficient resource utilization.

    1. Multi-Cloud Big Data Analytics:

    In this project, you will utilize AWS EMR, Azure HDInsight, and GCP Dataproc for big data processing and analytics. Imagine you are working for a retail company that wants to gain insights from large volumes of customer data collected from various sources. Your objective is to build a multi-cloud big data analytics solution that processes, analyzes, and visualizes the data. You will leverage cloud-based big data processing services such as AWS EMR, Azure HDInsight, and GCP Dataproc to handle the data processing tasks efficiently. By building and deploying data pipelines across multiple cloud providers, you ensure the scalability, reliability, and cost-effectiveness of the analytics solution.

    These projects provide practical scenarios where multi-cloud solutions are employed to address real-world challenges across different domains. By working on these projects, you will gain valuable hands-on experience in implementing multi-cloud architectures and leveraging the capabilities of AWS, Azure, and GCP for diverse use cases.



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    Comprehensive Multi-Cloud Bootcamp: Mastering AWS, Azure, and GCP
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