Where you are: you ship code and you can call an LLM. The gap: everything an agent actually executes through — files, CLI tools, Linux utilities, Docker, GitHub Actions, AWS, CI/CD, testing, infrastructure automation, MLOps, AIOps. Where this takes you: automation you can put under a production AI system and defend in review. The path: 13 self-paced sections, one layer at a time.
For engineers who already ship software and now need the layer underneath AI systems — not a Python 101, not a hype course.
Every action an agent takes is a file operation, a CLI call, a container, a pipeline, a test, or a cloud resource. When that layer is thin, the demo still runs and the system still falls over. Read the list below and mark what is true for you today.
The code works in a cell. Turning it into a module someone else can run is another job.
Moving, parsing and orchestrating real project files at scale is still ad-hoc.
Wrapping a workflow in a clean CLI, or driving Linux from Python, is not yet routine.
The Dockerfile runs. Layers, caching and image size are still guesswork.
The YAML gets written by trial and error, without a mental model of the runner.
IAM, S3, EC2, credentials and CI-to-cloud pipelines are someone else’s territory.
Automation scripts ship untested — no fixtures, no regression net.
Infrastructure gets provisioned by clicking through consoles, not through code.
The vocabulary is familiar. The operational discipline behind it is not yet yours.
None of that is an AI problem. It is automation, packaging, testing, deployment and operational discipline — the layer that decides whether your AI work survives contact with production.
The engineers who move fastest into production AI are the ones already fluent in the automation, packaging and operational plumbing it sits on. That fluency is what this stage is for.
AI engineering is not the LLM call. It is Python that automates real work — parsing files, running tools, wiring pipelines, packaging environments, testing behaviour, shipping to real infrastructure. The senior engineers we work with all had this layer first; the AI layer went on top of it.
You bring the engineering judgement. This adds the toolchain that carries it into Agentic AI, GenAI, RAG, MLOps and AIOps work — self-paced, hands-on, and framed the way the work actually arrives.
Two lists. Five minutes of honesty here saves you a wasted month.
By the end, you can build, package, test and deploy the automation an AI system runs on — and explain each choice to the engineers who have to maintain it.
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 do not just reason. They execute — and every execution path runs through this layer.
An agent calls tools, reads and writes files, runs workflows, shells out, hits APIs, manages configuration, talks to Dockerised services, triggers CI/CD, runs tests and reaches cloud infrastructure. Each of those is a Python automation problem wearing an AI label. This is where you learn to own that layer.
The whole map, nothing hidden. Expand any section to see exactly what you will build in it.
Each step assumes the one before it. By step ten the automation is muscle memory, and the Agentic AI work stops being blocked by plumbing.
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 needs — you can now build and defend.
Straight about what this stage covers, and what still comes after it.
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:
Get fluent here and the next stage is about architecture and trade-offs — not about why the pipeline will not run.
Six deliberate choices, each one aimed at what you will do on Monday rather than what looks good in a syllabus.
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 work the way production teams work — packaged, tested, versioned, deployed.
Meant as the foundation before Agentic AI, GenAI, RAG, AI Evals, and MLOps projects.
Source code for every demonstration, so you lift a working pattern into your own repository instead of rebuilding it from a video.
Self-paced access, source code and reference decks. No cohort date to wait for — you begin the day you decide.
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.
The questions experienced engineers ask before committing time to this.
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.
You have the engineering experience. Add the layer that turns an AI idea into something you can package, test, deploy and hand over. Stage 1 of the path — then Production, then Architecture.
Build the Automation Layer13 sections · 109 lectures · 14+ hours · self-paced access · source code included.
You already bring real engineering experience. These live programs add the production layer on top of it — without asking you to start over.
Eight live weeks. One production-style Agentic AI system you build end to end — orchestration, governed tools & MCP, production RAG, async execution, evaluation, security, deployment — and every decision something you can defend. Nothing else required first: Python and LangChain foundation bonuses included free.
Secure Your Seat →Once you can ship the system, the harder question is which system to build. Discovery, scoping, an architecture you can defend, evaluation, delivery and adoption — twelve weeks of live case labs. Reserved for Diamond Members; not sold separately.
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