End-to-end-Anomalies-ML-Project

Creating a custom ML project and then deploying it in an environment for testing and further observations of Industrial Data.

Overview

This project aims to develop an end-to-end system for processing streaming data from IoT sensors and detecting anomalies within the data. The system includes data engineering pipelines, machine learning model development, deployment infrastructure setup, and monitoring solutions. The README file provides an overview of the project structure, setup instructions, and key components.

Table of Contents

• Project Description
• Folder Structure
• Setup Instructions
• Data Processing Pipelines
• Machine Learning Model Development
• Deployment and Monitoring
• Challenges and Solutions

Project Description

The project implements a comprehensive system for handling streaming data from IoT sensors, performing data cleaning and preprocessing, developing machine learning models for anomaly detection, deploying models into production environments, and setting up monitoring solutions for real-time insights.

Folder Structure

• Predictive maintenance for industrial machine: Contains all the scripts and notebooks for ML development.
• config.ini: Configuration file for database connections and other settings.
• cleaned_machine_data.csv: Cleaned data ready for model training.
• README.md: This file.

Data Processing Pipelines

The data_processing folder contains scripts and notebooks to handle streaming data from IoT sensors. It includes functions for data cleaning, preprocessing, and storing data in databases.

Machine Learning Model Development

The model_development folder includes scripts and notebooks for developing machine-learning models for anomaly detection. It covers feature engineering, model training, evaluation, and hyperparameter tuning.

Deployment and Monitoring

The deployment folder contains scripts and configurations for deploying models into production environments. It also includes setup instructions for monitoring solutions like Grafana.

Challenges and Solutions

The project addresses contemporary challenges such as real-time data processing and data security. Solutions include using distributed processing frameworks, encryption, access controls, and compliance with data privacy regulations.