Abhi0323 / Agile-MLOps-Deployment-Docker-AWS-CI-CD-Pipeline

An end-to-end predictive maintenance application using machine learning to enhance industrial efficiency. This project employs robust modular architecture and advanced MLOps practices, including Docker and AWS for scalable, real-time maintenance predictions.

Predictive Maintenance Application: Agile MLOps Deployment: Docker-AWS-CI/CD Pipeline

You can view my blog, which details the end-to-end steps for this project, here: https://medium.com/@abhishekgoud1212/building-an-end-to-end-predictive-maintenance-machine-learning-application-mlops-ecff82f5d103

1. Introduction

This project is an exemplary demonstration of applying advanced machine learning (ML) techniques and best practices in a real-world predictive maintenance system. It features a sophisticated end-to-end ML pipeline that integrates MLOps practices to ensure scalability, maintainability, and efficient deployment in production environments.

2. Project Setup

• Version Control and Collaboration: Leveraged GitHub for robust version control, enabling seamless collaboration and code management.

• Environment Consistency: Established a controlled Python environment with virtualenv, detailed in a requirements.txt file for replicability across development and production stages.

3. Advanced Architectural Design

• Modular Architecture: Adopted a modular design, segregating functionalities into distinct modules—data ingestion, transformation, model training, and prediction—to manage complexities effectively and enhance scalability.

• Exception Handling and Systematic Logging: Implemented comprehensive exception handling and a logging system to ensure high application reliability and operational transparency for real-time monitoring.

4. Technical Implementation Phases

• Robust Data Handling: Engineered a highly efficient data ingestion and transformation framework using Python’s dataclasses and scikit-learn pipelines, ensuring data integrity and consistency.

• Exploratory Data Analysis (EDA) and Feature Engineering: Performed deep exploratory analysis and innovative feature engineering to inform and optimize model selection and hyperparameter tuning.

• Advanced Model Training Techniques: Deployed multiple machine learning models, utilizing cross-validation and grid search for hyperparameter optimization. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to select the optimal model.

• Predictive Pipeline: Constructed a sophisticated prediction pipeline capable of processing real-time data inputs and generating predictions with high accuracy and speed.

5. MLOps and CI/CD Integration

• Continuous Integration/Continuous Deployment (CI/CD): Established a CI/CD pipeline using GitHub Actions to automate testing, building, and deployment phases, significantly accelerating the development cycle and ensuring high-quality releases.

• Docker and AWS Deployment: Utilized Docker for application containerization, achieving consistency across various environments. Integrated with AWS services, including EC2 and ECR, to facilitate a scalable and secure deployment.

6. Web Application and User Interaction

• Flask Application: Developed a dynamic Flask web application to serve the predictive maintenance system, integrating the backend ML model with a frontend interface.

• User-Friendly Design: Crafted responsive HTML templates and CSS styling to provide a seamless and engaging user experience, enabling easy interaction with the predictive system.

7. Conclusion

This project not only demonstrates the effective deployment of a high-performance predictive maintenance application but also showcases the integration of cutting-edge MLOps practices and technologies. It stands as a testament to the potential of combining machine learning innovation with robust software engineering and operational best practices to solve complex real-world problems.