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Python Automation for Agentic AI & MLOps

Build the Python, CLI, Docker, CI/CD, AWS & Testing Foundations Needed for Production AI Systems

Welcome to the official course repository for Python Automation for Agentic AI & MLOps by Manifold AI Learning.

This repository contains the source code, hands-on examples, scripts, configuration files, and supporting resources used throughout the course.

Course Overview

Modern AI systems are not just prompts and model calls.

Production-style Agentic AI, GenAI, MLOps, DevOps, and AIOps workflows often require strong automation foundations:

  • Python scripting
  • file and folder automation
  • command-line automation
  • Linux utilities
  • package management
  • Docker
  • GitHub Actions
  • AWS basics
  • CI/CD workflows
  • testing with Pytest
  • infrastructure automation
  • MLOps and AIOps foundations

This course is designed to help learners build those foundations in a practical, hands-on way.

Who This Course Is For

This course is useful for:

  • Software engineers moving into AI engineering
  • Backend engineers preparing for production AI systems
  • DevOps engineers learning Python automation
  • MLOps learners strengthening their automation foundations
  • QA/Test engineers moving into AI testing and automation
  • Data/ML professionals who want to become stronger with scripts, tools, testing, and deployment workflows
  • Learners preparing for Agentic AI, GenAI, RAG, MLOps, and production AI projects

What This Course Is Not

This is not a dedicated Agentic AI implementation course.

This is also not a prompt engineering course.

Instead, this is the Python automation foundation layer that supports production-style AI and automation workflows.

Before building serious Agentic AI or GenAI systems, learners should be comfortable with Python, files, CLI tools, Docker, CI/CD, testing, AWS basics, and automation workflows.

Course Structure

The course contains:

  • 13 sections
  • 109 lectures
  • 14+ hours of structured training

Curriculum

1. Introduction

  • Introduction
  • What Makes This Course Unique?
  • Slide Resources and Source Code

2. Python Essentials for DevOps, MLOps & AIOps

  • Introduction to Python
  • Installing and Running Python
  • Variables and Data Types in Python
  • Jupyter Lab Interface Quick Tour
  • Variables and Data Types - Hands On
  • Comments in Python Programming Language
  • Operators in Python Programming
  • Operators in Python - Hands On
  • Built-in Functions in Python Programming
  • Built-in Functions in Python Programming - Hands On
  • Built-in Functions in Python Programming - Part 2 - Hands On
  • Sequences in Python
  • Hands On Python Strings - Sequence Operations
  • Hands On Python List - Sequence Operations
  • Hands On Python Tuple - Sequence Operations
  • Hands On Python Dictionary - Sequence Operations
  • Hands On Python Sets - Sequence Operations
  • Hands On Python Range - Sequence Operations
  • Execution Control in Python
  • Hands On - Conditional Statements in Python
  • Hands On - For Control Statements in Python
  • Hands On - While Control Statements in Python
  • Hands On - Loop Control Statements in Python Programming
  • Exception Handling in Python
  • String Formatting in Python
  • String Formatting - Hands On
  • User Defined Functions in Python
  • User Defined Functions & Scope of Variables Hands On
  • Anonymous Functions - Lambda
  • Advanced Functions - map, filter, list & dict comprehension
  • Modules in Python
  • Modules in Python - Hands On
  • Regular Expressions
  • Regular Expressions Hands On
  • Introduction to Object Oriented Python
  • Hands On - Classes and Objects
  • Object Oriented Concepts in Python
  • Object Oriented Concepts - Hands On
  • Section Summary

3. Python File Automation - Working with Files and Filesystem

  • Introduction to Python File Automation
  • Working with Files and Directory
  • Working with Text Files
  • Working with Binary Files
  • Working with Common File Formats in DevOps, MLOps, AIOps Projects
  • Working with Common File Formats in DevOps, MLOps, AIOps Projects - Part 2
  • Strategies for Working with Large Files
  • Encryption and Cryptography using Python
  • Working with Directories in Python - os, shutil, pathlib
  • Examples from MLOps

4. Command Line Automation - DevOps, MLOps & AIOps

  • Introduction to Working with Command Lines
  • Working with sys Module - Hands On
  • Working with os Module
  • Working with subprocess Module
  • Working with Command Line Tools
  • sys.argv - Command Line Inputs
  • Argparse - Parsing Command Line Inputs
  • Function Decorators
  • Parsing the Command Line using Click
  • Creating a More Complex CLI using Click
  • Working with Fire Package

5. Linux Utilities with Python

  • Introduction to Python Fabric Library
  • Hands On Python Fabric
  • Monitor the System with psutil
  • Hands On psutil

6. Python Package Management

  • Introduction to Python Package Management
  • Hands On Package Management with Python
  • Hands On MLOps Package to PyPI

7. Docker for DevOps, MLOps & AIOps

  • Introduction to DevOps
  • Introduction to Docker
  • Docker Installation
  • Docker Hands On

8. GitHub Actions for Python Projects

  • Introduction to GitHub Actions
  • Quick Demo on GitHub Actions YAML File
  • Understanding GitHub Actions YAML File
  • Create GitHub Actions from Scratch
  • Configure Workflow Based on Use Case

9. Getting Started with AWS - Prep Work for CI/CD Pipeline for Python Projects

  • Agenda of the Section
  • Create AWS Account
  • Setting up MFA on Root Account
  • Create IAM Account and Account Alias
  • Setup CLI with Credentials
  • IAM Policy
  • IAM Policy Generator & Attachment
  • Delete the IAM User
  • S3 Bucket and Storage Classes
  • Creation of S3 Bucket from Console
  • Creation of S3 Bucket from CLI
  • Version Enablement in S3
  • Introduction to EC2 Instances
  • Launch EC2 Instance & SSH into EC2 Instances
  • Clean Up Activity

10. CI/CD Pipeline with GitHub Actions - AWS EC2 Instances

  • Agenda of the Section
  • Exploring the Files of CI/CD Python
  • Pre-requisite Setup for CI/CD Pipeline
  • Test the CI/CD with AWS

11. Pytest for MLOps & AIOps

  • Introduction to Pytest
  • Pytest Hands On
  • Pytest Fixtures

12. Infrastructure Automation using Python

  • Introduction to IaC
  • Introducing Pulumi
  • Getting System Ready
  • Pulumi Hands On
  • Pulumi with Advanced Use Case - EC2 with Security Group

13. MLOps / AIOps

  • Introducing MLOps
  • Hands On Demo MLOps
  • Testing the MLOps

Suggested Repository Structure

The course code is organized by section number and topic. Folder names in the repository map to the curriculum sections below.

python-for-agentic-ai-mlops-aiops-devops/
├── README.md
├── requirements.txt
├── .gitignore
├── 1.python-essentials/                    # Section 2 — Python Essentials
├── 2.automation-files-directory/           # Section 3 — File Automation
├── 3.working-with-command-line/            # Section 4 — Command Line Automation
├── 4.linux-utilities/                      # Section 5 — Linux Utilities
├── 5.package-management-python/            # Section 6 — Package Management
├── 6.docker-devops-mlops-aiops/            # Section 7 — Docker
├── 7.github-actions/                       # Section 8 — GitHub Actions
├── 8.ci-cd-python/                         # Section 10 — CI/CD with GitHub Actions & AWS
├── 9.pytest-devops-mlops/                  # Section 11 — Pytest
├── 10.infrastructure-management-python/    # Section 12 — Infrastructure Automation (Pulumi)
├── 11.python-mlops/                        # Section 13 — MLOps / AIOps
└── 12.monitoring-python-prometheus/        # Bonus — Prometheus & Grafana monitoring
Curriculum section Repository folder
1. Introduction Slides and overview (no dedicated code folder)
2. Python Essentials 1.python-essentials/
3. File Automation 2.automation-files-directory/
4. Command Line Automation 3.working-with-command-line/
5. Linux Utilities 4.linux-utilities/
6. Package Management 5.package-management-python/
7. Docker 6.docker-devops-mlops-aiops/
8. GitHub Actions 7.github-actions/
9. AWS Basics Console/CLI walkthroughs (no dedicated code folder)
10. CI/CD Pipeline 8.ci-cd-python/
11. Pytest 9.pytest-devops-mlops/
12. Infrastructure Automation 10.infrastructure-management-python/
13. MLOps / AIOps 11.python-mlops/
Bonus 12.monitoring-python-prometheus/

Setup Instructions

1. Clone the Repository

git clone <repo-url>
cd python-for-agentic-ai-mlops-aiops-devops

2. Create a Virtual Environment

For macOS/Linux:

python3 -m venv .venv
source .venv/bin/activate

For Windows:

python -m venv .venv
.venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Run Python Scripts

Navigate to the relevant section folder and run the examples.

Example:

cd 3.working-with-command-line
python demo.py

5. Run Tests (Optional Validation)

After installing dependencies, you can validate key sections with:

# Section 11 — Pytest examples
cd 9.pytest-devops-mlops
pytest

# Section 6 — MLOps package tests
cd 5.package-management-python/mlops-package
PYTHONPATH=. pytest

# Section 13 — MLOps project tests
cd 11.python-mlops/mlops_package
pytest

# Section 10 — CI/CD Flask app unit test
cd 8.ci-cd-python
python tests.py

Some sections (AWS console demos, Pulumi, Fabric remote tasks, Prometheus/Grafana) require additional cloud setup, credentials, or optional packages noted in requirements.txt.

Environment Variables

Some examples may require credentials or environment variables.

Create a .env file only when required.

Example:

AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_DEFAULT_REGION=ap-south-1

Never commit .env files, access keys, passwords, tokens, or secrets to GitHub.

Important AWS Note

Some AWS demos may create cloud resources such as S3 buckets, EC2 instances, IAM users, or related services.

Please clean up resources after completing the demo to avoid unexpected charges.

Always follow your organization’s cloud security policies when working with AWS credentials.

How to Use This Repository

Recommended learning flow:

  1. Watch the course video for the section.
  2. Open the corresponding section folder.
  3. Read the section notes or comments in the code.
  4. Run the hands-on examples.
  5. Modify the examples with your own inputs.
  6. Complete the practice task if provided.
  7. Clean up any generated files or cloud resources.

Why This Matters for Agentic AI

Agentic AI systems often depend on automation-heavy workflows.

For example, agents may need to:

  • read and write files
  • call tools
  • execute scripts
  • trigger workflows
  • interact with APIs
  • run CLI commands
  • manage configuration
  • use Dockerized environments
  • trigger CI/CD pipelines
  • validate outputs through tests
  • connect to cloud infrastructure
  • monitor system behavior

This course helps build the Python automation foundation behind those workflows.

Recommended Next Learning Path

After completing this course, learners can move deeper into:

  • Agentic AI systems
  • RAG systems
  • AI evaluation and testing
  • MLOps and LLMOps
  • Production AI system design
  • AI workflow orchestration
  • Cloud deployment for AI systems

Explore the full Manifold AI Learning path at manifoldailearning.com/courses.

Continue Your Agentic AI Journey

This repository builds the Python automation foundation. When you are ready to go from scripts and CI/CD to production-grade Agentic AI systems, Manifold AI Learning offers structured next steps:

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Browse all programmes: https://manifoldailearning.com/courses

Requirements

Recommended:

  • Python 3.9+
  • Git
  • VS Code or any preferred IDE
  • Jupyter Lab or Jupyter Notebook
  • Docker Desktop
  • AWS account for AWS-related demos
  • Basic terminal/command-line familiarity

Disclaimer

This repository is provided for educational purposes as part of the course.

Before using any code in production, review, test, secure, and adapt it according to your project and organization’s standards.

Cloud-related examples may incur charges if resources are created and not cleaned up.

Instructor

Nachiketh Murthy Founder, Manifold AI Learning

Manifold AI Learning focuses on helping engineers move from tutorials and demos to production-style AI systems.

Brand philosophy: Systems ship. Demos don’t.

Support

For course-related support, please use the official Manifold AI Learning course platform or support channel.

License

This repository and its contents are intended for enrolled learners of the course.

Please do not redistribute, resell, or publish the course materials without permission from Manifold AI Learning.

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