Build an AI Automated Ordering System with Python & AWS

Master Demand Forecasting, Docker, Lambda, and Logistics Logic. bridging the gap between AI modeling and real-world SCM

What you’ll learn

• Build a serverless AI application using Python, Docker, and AWS Lambda.
• Implement machine learning demand forecasting using Scikit-learn and Pandas.
• Design real-world logistics logic, such as safety stock and lead time calculation.
• Automate data workflows using DynamoDB Streams and S3 for audit logging.

Requirements

• A Google account (to use Google Colab) and an AWS account (free tier is sufficient) are required.
• Basic knowledge of Python syntax and AWS is helpful, but not required. We will build everything step-by-step.
• No high-spec PC is required; all development is completed within the browser (CloudShell & Colab).

Description

"I built an AI model, but I don’t know how to apply it to real business problems."

Does this sound familiar? This course is not just a programming tutorial; it is a practical development guide designed to solve real-world logistics challenges using AWS and Python.

We bridge the gap between "theoretical AI" and "practical business systems." You will learn how to integrate messy, real-world constraints-such as "long lead times for overseas procurement" or "reducing inventory during the rainy season to prevent rust"-into your system architecture.

Course Highlights:

• Browser-Based Development: By using Google Colab and AWS CloudShell, you can complete the entire development flow without complex local environment setups.
• Serverless AI: We adopt AWS Lambda’s Container Image support to run heavy AI libraries (like Scikit-learn/Pandas) in a serverless environment.

Course Agenda:

• Section 1: Introduction – Course overview and system architecture.
• Section 2: Environment Setup – Setting up Google Colab and AWS CloudShell.
• Section 3: Data Strategy & Generation – Generating dummy sales data with seasonality and weather correlation using Python.
• Section 4: Implementing AI Logic (Google Colab) – Building demand forecasting models with Scikit-learn.
• Section 5: Implementing Business Logic (Google Colab) – Coding rules for "Order Judgment" and "Safety Stock."
• Section 6: Containerization & AWS Deploy (CloudShell) – Building Docker containers, pushing to ECR, and creating Lambda functions.
• Section 7: Simulation & Testing – Scenario testing via API integration.
• Section 8: Summary & Advanced Topics – Audit logging with DynamoDB Streams, weather API implementation, and model expansion.

About the Instructor: Maruchin Tech

Currently, I operate independently in the EdTech sector and create educational content on Cloud and Programming as a Udemy Instructor. Credentials: AWS All Certifications (12 Certifications as of 2025).

Who this course is for:

• Python learners who want to move beyond basic syntax and build practical business applications.
• Supply chain or logistics professionals who want to understand how AI and Cloud technology can improve operations.
• Engineers interested in Serverless architecture, Docker containers on Lambda, and MLOps basics.

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