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Build an AI Automated Ordering System with Python & AWS
29 students
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.
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Course content
8 sections • 33 lectures • 2h 27m total length
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.
Business Logic Focus: Learn the design philosophy behind integrating AI predictions with strict business rules.
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
After majoring in Information Engineering, I started my career at a Japanese automotive manufacturer. I spent 7.5 years in Supply Chain Management (SCM), handling packaging, procurement, and purchasing. Following that, I worked as an IT Consultant for 6 years, specializing in manufacturing and logistics sectors, focusing on Inventory Management and ERP system development.
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.