Welcome to the Object Detection Project! This project combines the capabilities of Deep Learning with MLOps (Machine Learning Operations) to automate the process of detecting safety gears like glasses, helmet, jacket, boots, etc. By integrating MLOps practices, we ensure efficient model development, deployment, and monitoring, resulting in a reliable and scalable solution.
The Object Detection Project employs advanced deep learning techniques to identify safety gears in images, facilitating applications such as workplace safety monitoring. Beyond model building, this project focuses on implementing end-to-end MLOps pipelines to streamline model development, deployment, and maintenance.
The project is structured into several components, each fulfilling a specific MLOps function:
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Setup: Scripts and instructions for setting up the project environment, including creating a virtual environment, installing dependencies, and configuring AWS CLI for data storage and management.
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Data Ingestion: Components for fetching, loading, and storing image data from external sources (e.g., Kaggle) into an AWS S3 bucket for further processing.
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EDA and Feature Engineering: Notebooks for exploratory data analysis and feature engineering to extract relevant information and prepare the data for training.
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Data Validation: Components for validating the quality and integrity of the data to ensure that only reliable data is used for training.
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Model Training: Components for training deep learning models on the processed data, including data augmentation, hyperparameter tuning, and model evaluation.
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Model Evaluation: Components for evaluating the performance of trained models using appropriate metrics and selecting the best-performing model for deployment.
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Model Deployment: Scripts, Dockerfiles, and instructions for deploying the selected model as a scalable and reliable service, leveraging Docker and AWS services for hosting and management.
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CI/CD Pipeline: Configuration files and workflows for implementing a continuous integration and continuous deployment (CI/CD) pipeline, automating testing, building Docker images, and deploying models with every code change.
This project highlights best practices in MLOps implementation, including:
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Dockerization: Containerizing the model training and deployment processes using Docker to ensure consistency and reproducibility across different environments.
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CI/CD Pipeline: Setting up a robust CI/CD pipeline with GitHub Actions to automate testing, building Docker images, and deploying models to production environments.
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Model Pusher: Implementing a model pusher component to automatically push trained models to an AWS S3 bucket for versioning and storage.
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AWS Integration: Leveraging AWS services such as S3, EC2, and ECR for model hosting, storage, and infrastructure management, ensuring scalability and reliability of the deployed solution.
To get started with the project and experience the power of MLOps in action, follow these steps:
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Set up your project environment by creating a virtual environment and installing dependencies from the
requirements.txtfile. -
Configure AWS CLI and create an S3 bucket for storing image data.
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Fetch the image data from the provided source and upload it to the AWS S3 bucket using the provided scripts.
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Explore the data using EDA notebooks and perform any necessary preprocessing or feature engineering.
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Train deep learning models using the provided scripts and components, and evaluate their performance using appropriate metrics.
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Deploy the best-performing model for real-time predictions using Docker and AWS services.
Contributions to the project are welcome! If you have any suggestions, bug reports, or feature requests related to MLOps practices, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.