What's included

• 1. Data Processing & Exploration

  ✅ Cleaned and preprocessed dataset (CSV, Parquet, or database storage) ✅ Data preprocessing scripts (Python scripts or Jupyter Notebooks) ✅ Exploratory Data Analysis (EDA) report with visualizations and insights

• 2. Model Development & Training

  ✅ Feature engineering and selection report ✅ Baseline model with performance metrics ✅ Trained machine learning model with optimal hyperparameters ✅ Model evaluation report (precision, recall, accuracy, AUC, etc.)

• 3. Model Deployment & Integration

  ✅ Packaged model in a deployable format (ONNX, TensorFlow SavedModel, PyTorch, etc.) ✅ REST API for model inference (FastAPI/Flask/Django) ✅ Deployment setup on cloud (AWS, Azure, GCP) or on-premise ✅ Docker container with all dependencies for easy deployment

• 4. Performance Optimization & Scalability

  ✅ Optimized model for faster inference (quantization, pruning, distillation) ✅ Caching mechanism for reducing latency (Redis, Memcached) ✅ Scalable pipeline for retraining and updating the model

• 5. Monitoring & Maintenance

  ✅ Logging and monitoring setup (Prometheus, ELK Stack) ✅ Model drift detection system with retraining triggers ✅ Scheduled model updates and performance reports

• 6. Documentation

  ✅ Codebase with clear documentation (README, docstrings) ✅ API documentation (Swagger/OpenAPI) ✅ User guide for model usage and integration ✅ Final report with project summary and recommendations