AI Content Classifier: Production MLOps Pipeline

A production-ready MLOps pipeline that automatically classifies Reddit content using advanced multi-label machine learning with enterprise-level automation, experiment tracking, and continuous learning.

🌐 Live Application

🎯 Project Overview

• Multi-Label Classification: Simultaneous analysis across 5 dimensions (Safety, Toxicity, Sentiment, Topic, Engagement).
• Automated MLOps: Weekly retraining with automated champion model selection.
• Experiment Tracking: Full integration with MLflow & DagsHub to track metrics (F1-score, Accuracy) for every run.
• Production Scaling: Handles 25,000+ posts per training cycle.
• Real-time Inference: Sub-second response times using optimized .joblib model serialization.

📊 Multi-Label Classification

Category	Description	Classifications
Safety	Content safety assessment	Safe, NSFW
Toxicity	Harmful content detection	Non-toxic, Toxic
Sentiment	Emotional tone analysis	Positive, Neutral, Negative
Topic	Content categorization	Technology, Gaming, Business, Health
Engagement	Viral potential prediction	High, Low Engagement

🏗️ Architecture

MLOps Pipeline:

1. Data Collection: Weekly automated Reddit data ingestion (25,000+ posts).
2. Feature Engineering: TF-IDF vectorization (10k features, 1-2 grams).
3. Multi-Model Training: Simulates 5 different algorithms (Logistic Regression, SVM, Naive Bayes, LightGBM, MLP) simultaneously.
4. Experiment Tracking: Logs all parameters and metrics to DagsHub/MLflow.
5. Champion Selection: Automatically compares F1-scores and selects the single best model for deployment.
6. Deployment: Automated Git LFS versioning and cloud deployment.

🎯 Performance Metrics

Metric	Value	Description
Binary F1-Score	88.3%	SFW/NSFW classification accuracy
Multi-Label Jaccard	82.7%	Overall multi-category performance
Training Data	25,000+	Reddit posts per training cycle
Inference Speed	<100ms	Real-time response capability
Model Size	~50MB	Optimized .joblib compression
Automation	Weekly	Continuous learning and updates

🛠️ Technology Stack

Core Technologies:

• Python 3.11, Scikit-learn, LightGBM, Pandas, NumPy
• Streamlit, Plotly (Visualization)
• PRAW (Reddit API), TF-IDF (NLP)

MLOps & Infrastructure:

• MLflow (Experiment Tracking), DagsHub (Remote Storage)
• GitHub Actions (CI/CD), Git LFS (Model Versioning)
• Joblib (Efficient Model Serialization)

🚀 Local Development Setup

Prerequisites

• Python 3.11+
• Git with Git LFS support
• Reddit API credentials (for data collection)
• DagsHub Account (for experiment tracking)

Step 1: Clone Repository

git clone https://github.com/RobinMillford/Reddit-content-classifier.git
cd Reddit-content-classifier

# Setup Git LFS for model files
git lfs install
git lfs pull

Step 2: Environment Setup

# Create virtual environment
python -m venv myenv

# Activate virtual environment
# Windows:
myenv\Scripts\activate
# macOS/Linux:
source myenv/bin/activate

# Install dependencies
pip install -r requirements.txt

Step 3: Configuration

Create a .env file in the project root. You need both Reddit API keys (for data) and DagsHub keys (for tracking).

# Reddit API (Data Collection)
REDDIT_CLIENT_ID=your_reddit_client_id
REDDIT_CLIENT_SECRET=your_reddit_secret
REDDIT_USER_AGENT=YourAppName/1.0

# DagsHub/MLflow (Experiment Tracking)
DAGSHUB_OWNER=your_dagshub_username
DAGSHUB_REPO=your_dagshub_repo_name
DAGSHUB_TOKEN=your_dagshub_token_here
EXPERIMENT_NAME=your_experiment_name_here

Step 4: Run Application

# Start the web application
streamlit run app.py

🌐 Access: Application runs at http://localhost:8501

Step 5: Custom Model Training (Optional)

To run the pipeline manually and trigger the Automatic Champion Selection:

# 1. Collect fresh training data
python src/ingest_data.py

# 2. Train 10 models, log to DagsHub, and save the best one locally
python src/train.py

📁 Project Structure

├── src/
│   ├── ingest_data.py           # Reddit data collection script
│   └── train.py                 # ML training & Auto-selection logic
├── .github/workflows/           # CI/CD automation
├── app.py                       # Streamlit web application
├── best_binary_model.joblib     # The Champion Binary Model (Git LFS)
├── best_multi_model.joblib      # The Champion Multi-Label Model (Git LFS)
├── tfidf_vectorizer.joblib      # Text preprocessing pipeline (Git LFS)
└── model_metadata.joblib        # Model labels & encoders (Git LFS)

💼 Professional Impact

Business Value: Demonstrates end-to-end ML engineering capabilities with production-ready automation, remote experiment tracking, and scalable infrastructure design.

Technical Expertise: Showcases expertise in MLOps, MLflow integration, automated pipelines, multi-label classification, and cloud deployment strategies.

Results Delivered: 88%+ accuracy system processing 25,000+ posts weekly with zero-downtime continuous deployment.

🤝 Contributing

This project is open source and welcomes contributions from the community.

How to Contribute:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/enhancement
3. Make your changes with proper testing
4. Submit a pull request with detailed description

Project Repository: github.com/RobinMillford/Reddit-content-classifier

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This project demonstrates production-ready MLOps implementation suitable for enterprise content moderation systems.