Case study
ML-Based Price Prediction Engine for Optimizing Supply, Demand, and Pricing
To build an ML-driven predictive pricing engine that could accurately forecast monthly supply, demand, and pricing for different food product categories, improve decision-making, and reduce time and cost spent on manual estimations.
Table of contents
Business objective
To build an ML-driven predictive pricing engine that could accurately forecast monthly supply, demand, and pricing for different food product categories, improve decision-making, and reduce time and cost spent on manual estimations.
Solutions & implementation
Developed a Machine Learning-based price prediction engine on Amazon SageMaker for real-time estimation of optimal product pricing.
Integrated the solution with a CI/CD pipeline using Amazon CodePipeline and CodeCommit for seamless model retraining and deployment.
Cleaned and engineered data to identify key pricing factors across commercial, retail, and international customer segments.
Deployed the entire workflow using Docker, Lambda, and API Gateway for scalable and serverless execution.
Hosted services on AWS EC2 with APIs exposed via Flask for model interaction and consumption.
Major technologies used
Amazon SageMaker
ML training and deployment platform
Amazon CodePipeline, CodeCommit, ECR
For CI/CD orchestration
AWS Lambda, EC2, API Gateway
For serverless and scalable API services
Python, Flask, Docker
Core language and application framework stack
Business outcomes
Automated Optimal Price Estimation
Enabled accurate price prediction across all food categories, reducing manual effort.
Improved Forecasting Efficiency
Used ML models to augment and refine monthly supply-demand predictions.
CI/CD Enabled Retraining Pipeline
Deployed a flexible retraining mechanism with AWS CodePipeline for scalable model updates.
Cost and Time Savings Through Automation
Replaced manual market forecast processes with a data-driven, ML-based system.
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