DevOps & AWS
This course provides a hands-on approach to implementing DevOps practices and automating software delivery using AWS services and industry tools like Docker, Kubernetes, Jenkins, Terraform, and more.
Objectives
- Understand DevOps culture, CI/CD, and automation
- Master tools like Git, Jenkins, Docker, Kubernetes, and Ansible
- Manage infrastructure with IaC using Terraform & AWS CloudFormation
- Work with AWS services: EC2, S3, EKS, IAM, CloudWatch, Lambda
- Implement monitoring, logging, and cost optimization
Training Approach
The DevOps & AWS course emphasizes experiential learning through real-time implementation of DevOps pipelines and cloud infrastructure. Learners work in cloud-based environments using AWS Free Tier and tools like Git, Jenkins, Docker, Kubernetes, Terraform, and Ansible. Each concept is taught with hands-on labs and mini-projects that simulate real-world deployment and automation scenarios. The course progressively builds from foundational DevOps practices to advanced cloud-native strategies, ensuring learners gain practical skills in CI/CD, monitoring, infrastructure as code, and scalable cloud deployments.
Course Highlights
- End-to-end CI/CD pipeline creation
- Containerization with Docker and orchestration with Kubernetes
- Cloud-native development on AWS
- DevOps for microservices and serverless architecture
- Real-time deployment and rollback strategies
Career Opportunities
- AI Engineer / Researcher
- Machine Learning Engineer
- Data Scientist
- NLP Engineer
- Generative AI Specialist
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Do I need a math background?
A basic understanding of linear algebra, statistics,and calculus is helpful.
What programming languages are used?
Primarily Python and associated AI/ML libraries.
Will I get to build my own GPT-like model?
Yes, we cover both using existing APIs and building simplified versions.
Is this course for beginners?
Suitable for beginners with some programming experience.
Do we learn about model fine-tuning?
Yes, fine-tuning pretrained models is included.
Will we deploy AI models to production?
Yes, using tools like Flask, FastAPI, or Streamlit, and deploying to cloud platforms.