Chicken Disease Classifier Using MLOPS DVC Pipeline
Developed an end-to-end machine learning solution for automated chicken disease classification using Convolutional Neural Networks (CNN).
The project implements a complete MLOps pipeline with Data Version Control (DVC) to enable efficient model versioning, deployment,
and monitoring. The system can identify and classify common chicken diseases from images, enabling early detection and timely intervention
in poultry farming operations.
🎯 Key Achievement
Achieved 93% accuracy across 5+ common chicken diseases while reducing detection time by 80%.
Implemented a fully automated MLOps pipeline with DVC and GitHub Actions, resulting in 65% faster iteration cycles
and 99% reproducibility of model experiments.
Poultry farming faces significant challenges in early disease detection, which can lead to:
- Rapid spread of infections across flocks, causing massive economic losses
- Delayed treatment due to manual inspection inefficiencies
- High mortality rates from late-stage disease identification
- Lack of automated, scalable disease monitoring systems
- Need for expert knowledge that may not always be available
🧠 CNN-Based Disease Classification
- Built a deep Convolutional Neural Network using TensorFlow/Keras
- Trained on dataset of chicken images with 5+ disease categories
- Implemented transfer learning techniques for improved accuracy
- Applied data augmentation to handle limited dataset sizes
- Achieved 93% classification accuracy on test dataset
- Reduced disease detection time from hours to seconds (80% improvement)
🔄 MLOps Pipeline with DVC
Implemented a comprehensive MLOps workflow for production-ready deployment:
- Data Version Control (DVC): Track datasets, models, and experiments
- Pipeline Automation: Automated data preprocessing, training, and evaluation stages
- Model Versioning: Version control for all model artifacts and hyperparameters
- Experiment Tracking: Systematic logging of metrics, parameters, and results
- Reproducibility: 99% reproducibility across different environments
- Collaboration: Seamless team collaboration with shared DVC remote storage
🚀 CI/CD with GitHub Actions
Automated deployment pipeline for continuous integration and delivery:
- Automated Testing: Unit tests and integration tests on every commit
- Model Validation: Automatic accuracy and performance validation
- Docker Containerization: Consistent deployment environments
- AWS Deployment: Automated deployment to AWS cloud infrastructure
- Rollback Capability: Quick rollback to previous stable versions
- Achieved 65% faster iteration cycles through automation
🌐 Web Application with Flask
- User-friendly Flask web interface for disease prediction
- Image upload functionality for real-time classification
- RESTful API endpoints for integration with external systems
- Confidence scores and disease recommendations
- Mobile-responsive design for field use
Model Architecture
- Framework: TensorFlow 2.x with Keras API
- Architecture: Custom CNN with multiple convolutional and pooling layers
- Activation: ReLU for hidden layers, Softmax for output layer
- Optimization: Adam optimizer with learning rate scheduling
- Loss Function: Categorical cross-entropy
- Regularization: Dropout and batch normalization to prevent overfitting
Data Pipeline
- Data Ingestion: Automated data collection and validation
- Preprocessing: Image resizing, normalization, and augmentation
- Data Augmentation: Rotation, flipping, zooming for dataset expansion
- Train-Test Split: Stratified splitting to maintain class distribution
- DVC Tracking: Version control for all data transformations
MLOps Infrastructure
- DVC: Data and model versioning with remote storage
- GitHub Actions: CI/CD pipeline automation
- AWS S3: Model artifact storage
- AWS EC2: Model deployment and serving
- Docker: Containerized application for consistent deployment
- Flask: Lightweight web framework for API and UI
- Real-Time Prediction: Instant disease classification from uploaded images
- High Accuracy: 93% accuracy across multiple disease categories
- Scalable Architecture: Can handle increasing data and user load
- Version Control: Complete tracking of data, models, and experiments
- Automated Pipeline: End-to-end automation from data ingestion to deployment
- Easy Deployment: Docker containerization for quick deployment
- API Integration: RESTful APIs for third-party integrations
Python
TensorFlow
Keras
CNN
DVC
Flask
MLOps
GitHub Actions
AWS EC2
AWS S3
Docker
Computer Vision
Deep Learning
- MLOps Best Practices: Implementing version control, automation, and monitoring in ML projects
- DVC Integration: Managing large datasets and models with version control
- CI/CD for ML: Building automated pipelines for model deployment
- CNN Optimization: Tuning hyperparameters and architecture for better accuracy
- Production Deployment: Deploying ML models to cloud infrastructure
- Data Augmentation: Handling limited datasets through augmentation techniques
- Expand to detect 10+ disease categories with more training data
- Implement mobile application for on-field disease detection
- Add video-based disease monitoring for real-time surveillance
- Integrate with IoT sensors for automated health monitoring
- Implement model retraining pipeline with new data
- Add multi-language support for global accessibility
- Deploy edge computing solution for offline predictions