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Machine Learningintermediate

Drinks Quality Prediction System

This project aims to build a robust, end-to-end Machine Learning pipeline for predicting the quality of Drinks based on physicochemical tests. It demonstrates a complete ML workflow, emphasizing modularity, reproducibility, and automation.

21 lectures

What You Will Learn

End-to-End ML Lifecycle: Master the complete journey from data ingestion and validation to model training and deployment.
Modular Coding: Learn to write clean, modular, and production-ready Python code structured for scalability.
MLOps Foundations: Understand key MLOps concepts including automated pipelines, reproducibility, and component-based architecture.
Cloud Deployment: Gain hands-on experience deploying containerized applications to AWS (EC2, ECR) using CI/CD pipelines.
CI/CD Automation: Implement continuous integration and deployment workflows using GitHub Actions.
API Development: Build and expose machine learning models via RESTful APIs using Flask.
Managing configuration using params.yaml and schema.yaml.

System Architecture

Drinks Quality Prediction System Architecture Diagram

High-level architecture overview of the Drinks Quality Prediction System .

What You'll Build

  • Drinks Quality Prediction System: A robust machine learning application capable of predicting the quality of drinks based on physicochemical properties.
  • Automated Training Pipeline: A multi-stage pipeline that handles Data Ingestion to Model Evaluation
  • Web Application: A user-friendly Flask-based web interface for real-time predictions.
  • Deployment Infrastructure: A Dockerized application ready for deployment on cloud platforms like AWS EC2.
  • CI/CD Automation: Implement continuous integration and deployment workflows using GitHub Actions.
Drinks Quality Prediction System
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