• views: 138
• date: 26 December 2025
• posted by: voska89

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.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 TechAfter 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.
Homepage

https://www.udemy.com/course/python-aws-forecasting-ai-en/

   
   
   

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