Python for DevOps with AI
Master Python for DevOps with AI, from scripting basics to automation and infrastructure management. Learn how to integrate Python with CI/CD, cloud platforms, and real-world DevOps tools to become job-ready in the tech industry, or combine it with our DevOps Engineer course and AWS Cloud course for a complete DevOps + cloud + AI path.
Python for DevOps Course Overview
Our comprehensive Python for DevOps course is designed to take you from beginner to advanced level across 20 detailed modules. You'll learn Python fundamentals, scripting for automation, infrastructure as code, CI/CD integration, and cloud deployment. The course features hands-on labs, real-world DevOps projects, and prepares you for in-demand roles in DevOps and cloud engineering, just like our DevOps Engineer course and Azure Cloud course.
Python for DevOps Key Highlights
Hands-on Coding
Learn by doing with extensive coding practice and real projects, just like in our DevOps and cloud courses.
100% Placement
Guaranteed job assistance until you get placed successfully, with support for Python, DevOps and cloud roles.
Portfolio Projects
Build impressive projects for your GitHub portfolio, including automation, CI/CD, and cloud integration.
Industry Certification
Get recognized certification valued by employers for Python and DevOps roles.
Affordable Fees
Quality Python education at competitive pricing with EMI options.
Flexible Learning
Weekend and weekday batches with personalized support for working professionals and students.
No Cost EMI
Easy payment options with zero additional charges and student-friendly plans.
Soft Skills Training
Communication, resume, and interview skills training to help you clear product and service-based company interviews.
Complete Python DevOps Syllabus
Practical Python DevOps curriculum covering Python basics, cloud automation, containers, CI/CD, and AI/LLM-powered DevOps automation.
Introduction to Python and DevOps
- Python history and why it is the language of choice for DevOps
- Simple Python program: “Hello DevOps”
- Shell vs Python: when to use each for automation
- Setting up Python environment (installation, VS Code, PyCharm)
- Running Python scripts in Linux and Windows environments
- Best practices for writing clean, maintainable DevOps scripts
Python Basics for Automation
- Variables in Python: naming conventions and scope
- Python data types: numeric, boolean, strings, lists, tuples, sets, dictionaries
- Operators in Python: arithmetic, assignment, comparison, logical, bitwise, identity, membership
- Conditional handling: if, else, elif, nested conditions
- Error handling: try, except, finally, custom exceptions
- Loops: for, while, loop control (break, continue, pass)
- Debugger basics and logging for troubleshooting scripts
Python Functions and Modules
- Defining and calling functions in Python
- Return statements and function reuse
- Basics of modules: importing and using built-in and custom modules
- Regular expressions (Regex): pattern matching, text manipulation, log parsing
- Working with files in Python: reading, writing, appending, context managers
Essential Python Modules for DevOps
- os module: file/directory operations, environment variables, OS commands
- subprocess module: running shell commands and external programs
- platform module: cross-platform compatibility and system info
- sys module: command-line arguments, exit codes, interpreter settings
- shutil module: high-level file operations (copy, move, delete)
- json module: reading and writing JSON files and API responses
- logging module: info, warning, error logs for production scripts
- requests module: making HTTP requests to REST APIs (Git, Jenkins, cloud)
- paramiko module: SSH automation, remote command execution, file transfer
- psutil module: system monitoring (CPU, memory, disk, network)
OOP and Advanced Python for DevOps
- Object-Oriented Programming (OOP): classes, objects, inheritance, encapsulation, polymorphism, abstraction
- Class variables vs instance variables
- Constructors (__init__) and destructors
- Decorators and their use in DevOps scripts
- Self, slots, and advanced class features
- Building reusable Python libraries for DevOps tasks
Python for Cloud Automation (AWS)
- Using boto3 to automate AWS services (EC2, S3, Lambda, RDS, IAM)
- Writing Lambda functions in Python for serverless automation
- Six real-world AWS automation use cases (cleanup, cost control, scaling, backups, security, reporting)
- Best practices for AWS credentials and security in Python scripts
Version Control and Git Automation
- Integrating Python scripts with Git and GitHub/GitLab
- Querying Git repositories using Python (GitPython)
- Automating branching, merging, and release workflows
- Automated code review and quality gate enforcement
- Release management, tagging, and changelog automation
Containerization and Orchestration with Python
- Docker container automation with Python (Docker SDK)
- Docker governance: image cleanup, tagging, and lifecycle policies
- Creating a Flask Python container and deploying it
- Kubernetes health monitoring using Python
- Kubernetes optimization and resource management scripts
Jenkins and CI/CD Integration
- Jenkins and Python integration for pipeline automation
- Docker remote repo cleanup project using Python with Jenkins
- AWS cost analyzer project for cost optimization
- Automating build, test, and deployment stages with Python scripts
Security and Infrastructure Scanning
- Creating a security app to scan infrastructure (servers, containers, cloud)
- Automated vulnerability scanning and compliance checks
- Secrets management and secure credential handling
- Building a DevOps security dashboard with Python
Introduction to AI and LLMs for DevOps
- What is AI and history of AI
- Different AI models available in the market (OpenAI, Anthropic, etc.)
- Hosting AI models locally on a PC
- Key characteristics of AI systems
- Large Language Models (LLMs) walkthrough and use cases in DevOps
RAG (Retrieval-Augmented Generation)
- Introduction to RAG (Retrieval-Augmented Generation) for DevOps
- How RAG enriches LLM prompts with external knowledge for grounded outputs
- Configuring a Vector DB for semantic search in chatbots
- Accessing Confluence and knowledge bases to solve real-world problems
- Connecting RAG and LLM to GitHub for repo access and actions
- Building a RAG system to connect databases and query them via natural language
LangChain and LangGraph for AI Agents
- LangChain and LangGraph overview: building AI agents
- Building an AI agent assistant that analyzes log alerts and auto-triggers remediation scripts (restart service, scale instances)
- Creating an AI-powered DevOps tool integrating FastAPI, CI/CD, Docker, and LLM automation
- Connecting Confluence to an AI agent to trigger alerts and auto-fix issues
- AI SRE: an agentic AI to diagnose and resolve high CPU and DB performance issues
- Building an RCA AI agent for incident analysis
MCP (Model Context Protocol)
- MCP overview and its role in AI agent communication
- Security and control in AI agents using MCP
- Deploying MCP server and MCP client
- Project: Complete DevOps AI automation project including Jira, containers, observability stacks, LLM, Confluence, and a human approval system using LangGraph and MCP
Interview Preparation and Real-World Scenarios
- Discussing real-world DevOps scenarios and tasks
- Agile methodology, sprint planning, and change execution methods
- Troubleshooting and day-to-day activities in DevOps roles
- Mock interviews with real-time Python and DevOps questions
- How to explain Python scripts and automation in interviews
- Resume and LinkedIn tips for Python DevOps roles
Frequently Asked Questions
Course Features
| Level | Beginner to Advanced |
| Class Type | Virtual & In-Person |
| Duration | 60 Days |
| Lectures | 80 |
| Labs/Projects | 85 |
| Total Sessions | 165 |
| Certificate | Yes |
| Daily Test | 1 daily Test |
| Weekly Test | 1 weekly Test |
| Mock Test | 8 mock Test |
Additional Benefits:
Start Your Python for DevOps with AI Journey Today!
Join our next batch and become a skilled Python DevOps engineer with our 20-module curriculum focused on scripting, automation, CI/CD, cloud integration and AI-powered DevOps workflows. Combine this with DevOps and AI Cloud DevOps to build a complete, future-proof tech career.
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