MCA - Cloud and DevOps
Overview
IBM Q²D is a global initiative offering specialized master’s programs in collaboration with top academic institutions. We are offering an advanced 2-year MCA program specializing in Cybersecurity, Ethical Hacking, and Digital Forensics.
S - Vyasa Deemed to be University located inside Sattva Global City, Kengeri, which is a NAAC A+, Category 1 University, would be the first university in India to execute this model of student engagement in partnership with IBM. The program is designed to equip students with cutting-edge skills, real-time exposure, and practical project experience, aligning with future workforce demands.
You've embarked on a cutting-edge master's program, crafted with industry leaders and delivered by IBM subject matter experts in a world-class learning environment.
Program Benefits Include
A Postgraduate Degree awarded by S-VYASA
An Advanced Certificate Digital Badge from IBM
Advanced Learning Certificate from Cambridge University Press and Assessment
This strategic academic-industry collaboration is designed to equip students with both foundational knowledge and practical skills in emerging domains, preparing them for high-impact careers in the digital era.
Campus Location
All programs are conducted at the futuristic S-VYASA Bangalore Campus, located inside Sattva Global City IT Park, Kengeri.
The "IBM ICE Advanced Certificate - Data Analytics" program is a comprehensive program designed to equip learners with the essential skills and knowledge to analyze data, derive insights,and support data-driven decision-making across various industries. Spanning approximately 200+ hours, this program covers foundational Python programming for data analysis, descriptive analytics and business intelligence, predictive and advanced analytical techniques, big data analytics, and the practical application of these skills in key business domains.
Fee Structure 2025-26
| Duration: 2 Years | I YEAR | II YEAR |
|---|---|---|
| Admission Fee | 15000 | - |
| Tuition Fee | 295000 | 280000 |
| Other Academic Fee | 5200 | 5200 |
| Total Fee | 315200 | 285200 |
Program Objectives
Upon successful completion of this program, learners will be able to:
- Understand the fundamental concepts of cloud computing, including service and deployment models, and the offerings of major cloud providers.
- Master containerization using Docker for application packaging and isolation.
- Implement and manage container orchestration using Kubernetes for scalable cloud deployments.
- Design, build, and manage automated CI/CD pipelines using industry-standard tools.
- Understand and implement Infrastructure as Code (IaC) using tools like Terraform and Ansible for cloud infrastructure management.
- Set up comprehensive monitoring and logging solutions for cloud environments.
- Implement performance optimization techniques for cloud workloads.
- Apply cloud security best practices to ensure secure deployments.
- Understand the considerations for deploying and managing AI/ML workloads on cloud platforms.
Program Summary This program is structured into five progressive modules that cover the core aspects of Cloud and DevOps.
Module 1
Introduces the foundational concepts of cloud computing.
Module 2
Focuses on containerization with Docker and orchestration with Kubernetes.
Module 3
Delves into building and managing CI/CD pipelines for automation.
Module 4
Covers the principles and implementation of Infrastructure as Code.
Module 5
Explores advanced cloud and DevOps practices including monitoring, optimization, security, and AI/ML on the cloud.
Learning Modules
- Fundamental concepts of cloud computing.
- Cloud service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS).
- Cloud deployment models: Public cloud, private cloud, hybrid cloud, multi-cloud.
- Key characteristics of cloud computing: On-demand self-service, broad network access, resource pooling, rapid elasticity, measured service.
- Overview of major cloud providers: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP) - basic service categories and offerings.
- Containerization concepts and benefits.
- Introduction to Docker: Dockerfiles, images, containers, networking, storage.
- Hands-on experience with Docker commands and basic Dockerfile creation.
- Introduction to Kubernetes: Architecture (master-node, worker-node), pods, deployments, services, namespaces.
- Deploying and managing containerized applications using Kubernetes.
- Scaling and updating applications in Kubernetes.
- Principles of Continuous Integration (CI) and Continuous Delivery (CD).
- Introduction to CI/CD tools: Jenkins, GitLab CI, Azure DevOps (one or more likely emphasized).
- Designing and building CI/CD pipelines for automated build, test, and deployment processes.
- Integrating version control systems (e.g., Git) with CI/CD pipelines.
- Automating software testing within CI/CD pipelines.
- Deployment strategies to cloud environments (e.g., blue/green, canary).
- Principles and benefits of Infrastructure as Code (IaC).
- Introduction to Terraform: Writing Terraform configurations to provision cloud resources (e.g., virtual machines, networks, storage).
- Introduction to Ansible: Writing Ansible playbooks for configuration management and application deployment.
- Comparing and contrasting Terraform and Ansible.
- Automating infrastructure provisioning and management in cloud environments.
Program Outcomes
Upon successful completion of this program, learners will be able to:
- Explain the fundamental concepts and models of cloud computing.
- Containerize applications using Docker and orchestrate them with Kubernetes.
- Design, build, and manage automated CI/CD pipelines using industry-standard tools.
- Implement Infrastructure as Code using Terraform and Ansible to manage cloud infrastructure.
- Set up basic monitoring and logging for cloud environments.
- Understand performance optimization and security best practices for cloud workloads.
- Identify key considerations for deploying AI/ML workloads on the cloud..
Skills Attained
Upon completion of this program, learners will gain the following skills:
- Cloud Computing Fundamentals: Understanding cloud models and providers.
- Containerization with Docker: Packaging and managing applications in containers.
- Container Orchestration with Kubernetes: Deploying, scaling, and managing containerized applications.
- CI/CD Pipeline Management: Designing and implementing automated software delivery pipelines.
- Infrastructure as Code (IaC): Provisioning and managing cloud infrastructure using Terraform and Ansible.
- Cloud Monitoring and Logging: Setting up basic monitoring and logging solutions.
- Cloud Performance Optimization: Understanding techniques for improving cloud workload performance.
- Cloud Security Basics: Applying fundamental security best practices in the cloud.
- Awareness of AI/ML on Cloud: Understanding key considerations for deploying AI/ML workloads.
