A practical self-paced course to help engineers build strong Python automation foundations across files, CLI tools, Linux utilities, Docker, GitHub Actions, AWS, CI/CD, testing, infrastructure automation, MLOps, AIOps, and production AI workflows.
Built for engineers moving into production AI systems — not a hype course, not a generic Python 101.
Agentic AI systems are not just prompts. They need automation, packaging, testing, deployment, infrastructure, and operational discipline. Skipping the foundation shows up later — in production.
Comfortable in notebooks, but writing production Python scripts feels foreign.
Struggle to move, parse, and orchestrate real project files at scale.
Cannot cleanly wrap workflows into CLIs, or drive Linux from Python.
Copy Dockerfiles from the internet, but don't understand what they do.
Wire up YAML by trial and error, without a real mental model.
IAM, S3, EC2, credentials, CI-to-cloud pipelines — still feel opaque.
Write "it works on my machine" scripts, no fixtures, no test discipline.
Provision by clicking through consoles, not through code.
Understand the words — but the actual operational discipline is missing.
Agentic AI systems are not just prompts. They need automation, packaging, testing, deployment, infrastructure, and operational discipline. That's what this course builds.
Before learners build production-style Agentic AI systems, they should be fluent with the automation, packaging, and operational plumbing that sits underneath them.
Modern AI engineering is not just about calling an LLM. It's about writing Python that automates real work — parsing files, running tools, wiring pipelines, packaging environments, testing behaviour, and shipping to real infrastructure. Every senior AI engineer we've mentored has one thing in common: they were fluent in this layer first.
This course strengthens exactly that layer, in a self-paced, hands-on, production-oriented way. By the end, you should be comfortable across the full Python automation toolchain that supports Agentic AI, GenAI, RAG, MLOps, and AIOps workflows.
Two lists. Read both honestly — a five-minute check now saves weeks later.
By the end of this course, you will be comfortable across the full Python automation toolchain that sits under Agentic AI, GenAI, MLOps, and AIOps systems.
Python fundamentals needed for automation
File & filesystem automation with os, shutil, pathlib
Working with text, binary, and common project file formats
CLI automation using sys, os, subprocess, argparse, Click, Fire
Linux automation with Fabric and psutil
Python package management & packaging workflows
Docker basics for Python and AI projects
GitHub Actions for Python project automation
AWS basics for CI/CD and automation workflows
CI/CD deployment to AWS EC2 using GitHub Actions
Pytest basics for testing automation workflows
Infrastructure automation using Pulumi
MLOps and AIOps foundations — enough operational discipline to plug into serious AI workflows.
Agents don't just think — they execute. Every action they take runs through the same automation layer you are about to learn.
Agentic AI systems constantly need to run real actions in the world — call tools, read and write files, run workflows, execute CLI commands, interact with APIs, manage configuration, use Dockerised services, trigger CI/CD, run tests, and connect to cloud infrastructure. Every one of those actions is Python automation, wrapped in production discipline. This course builds that foundation.
Every section, every lecture, every topic — no skips. Click any section to expand the full lecture list.
Each step builds on the previous one, so the automation muscle memory is deep by the time you touch Agentic AI systems.
Syntax, control flow, OOP — the language, done properly.
Text, binary, common DevOps/MLOps formats.
sys, os, subprocess, argparse, Click, Fire.
Fabric for remote automation. psutil for monitoring.
Ship repeatable Python; automate builds.
IAM, S3, EC2, CLI, and pipelines to real cloud.
Fixtures, structure, discipline for real code.
Provision cloud infra as versioned Python code.
Wire the operational discipline into your Python workflows.
Every tool call, file operation, deployment, and test an agent will need — you already know how to build.
We are direct about what this course is — and what it isn't.
This is not a dedicated Agentic AI implementation course. Instead, it builds the automation foundation that supports Agentic AI systems.
Agentic AI systems often depend on the following capabilities, all of which are ultimately Python automation problems dressed up in AI clothing:
This course strengthens those fundamentals so that when you move into serious Agentic AI, GenAI, RAG, or MLOps projects, you are not blocked at the plumbing layer.
Six deliberate design choices that separate this course from generic Python content.
Every topic is anchored to an automation, DevOps, MLOps, or AIOps use case.
Nearly every lecture is a hands-on demonstration, not a slide walk-through.
Files, CLI, Linux, cloud, CI/CD — framed as automation problems.
One course, four adjacent disciplines integrated into one path.
You leave more comfortable with the real workflows engineers use in production.
Meant as the foundation before Agentic AI, GenAI, RAG, AI Evals, and MLOps projects.
Structured content, hands-on demonstrations, and the reference material to go back to when you need it.
Practical foundation pricing. Self-paced access. Source code and resources included.
13 sections · 109 lectures · 14+ hours · hands-on Python demonstrations · source code & resources included.
Self-paced · foundation-focused · no hype, no guarantee — a practical course, honestly priced.
Direct answers to what comes up most often before enrolling.
It starts with Python essentials, but it is positioned toward automation for DevOps, MLOps, AIOps, and production AI workflows — not general-purpose Python 101.
No. It is a Python automation foundation course that supports learners who want to build Agentic AI, GenAI, MLOps, and automation-heavy systems later. This course strengthens the plumbing layer.
Basic programming familiarity helps, but the course includes Python essentials covering syntax, data structures, control flow, and OOP.
Yes — Docker basics and hands-on are included, framed around Python and AI projects.
Yes — GitHub Actions for Python projects is included, from YAML fundamentals to configuring workflows for real use cases.
Yes — AWS account setup, IAM, S3, EC2, CLI setup, and CI/CD preparation are included, plus a full CI/CD pipeline from GitHub Actions to AWS EC2.
Yes — Pytest basics and fixtures are covered so you can bring testing discipline to your automation workflows.
Yes — the course includes MLOps and AIOps foundation sections and examples. This is a foundation layer, not a specialised MLOps course.
It is a strong foundation course. Advanced Agentic AI, RAG, AI Evals, and production AI systems require additional focused learning on top of this course. Think of this as the base you should have before deeper AI work.
If you want to move into Agentic AI, GenAI, MLOps, AIOps, or production AI engineering, start by strengthening the automation layer. This is the base every serious AI engineer builds on.
Enroll Now13 sections · 109 lectures · 14+ hours · self-paced access · source code included.