# ML Engineer Assessment: Custom Model Fine-tuning Challenge

## 🎯 Scenario
You are tasked with building a **Customer Support Intent Classification System** for our e-commerce platform. The system should automatically categorize customer inquiries to route them to appropriate support teams.

## 📊 Dataset Provided
**BANKING77 Dataset** - Real banking customer service queries
- **Source**: HuggingFace `datasets` library (`banking77`)
- **Size**: 13,083 labeled customer queries
- **Classes**: 77 banking-related intents (card_arrival, transfer, balance, etc.)
- **Format**: `{'text': 'query', 'label': intent_id}`
- **Split**: You'll need to create train/validation/test splits
- **Domain**: Banking and financial services customer support

**Sample Data Points:**
```
"What is the base rate of the bank?" → get_exchange_rate
"I am still waiting on my card" → card_arrival  
"Can you help me make a payment?" → transfer
```

## 🎯 Your Mission
Build a complete fine-tuning pipeline for **Banking Customer Support Intent Classification** that demonstrates your ML engineering skills across the full lifecycle.

---

## 🔧 Technical Requirements

### Core Implementation (Must Have)
1. **Model Selection & Fine-tuning**
   - Choose and justify a pre-trained model (BERT, RoBERTa, DistilBERT, etc.)
   - Implement fine-tuning with proper hyperparameter configuration
   - Handle class imbalance if present

2. **Data Pipeline**
   - Clean and preprocess the provided dataset
   - Implement proper train/val/test splits (70/15/15)
   - Create data loaders with appropriate batching

3. **Training Infrastructure**
   - Implement training loop with proper logging
   - Add early stopping and learning rate scheduling
   - Track key metrics (accuracy, F1-macro, F1-per-class)

4. **Evaluation & Metrics**
   - Comprehensive evaluation on test set
   - Confusion matrix and classification report
   - Error analysis with examples

5. **Inference Demo**
   - Create a simple inference script/API
   - Demonstrate prediction on new examples
   - Show confidence scores

6. **Executable Pipelines** (Required)
   - **Training Pipeline**: End-to-end automated training with single command
   - **Inference Pipeline**: Batch or single prediction pipeline
   - **Evaluation Pipeline**: Automated model evaluation and reporting

7. **Jupyter Notebooks** (Required)
   - **Data Exploration**: EDA, class distribution, sample analysis
   - **Model Experimentation**: Different approaches, hyperparameter testing
   - **Results Analysis**: Performance analysis, error analysis, insights

### Additional  Features 
- Experiment tracking ( simple logging)
- Model versioning and checkpointing
- Hyperparameter optimization
- Simple web interface for testing

---

## 📋 Deliverables

### 1. Code Structure (Clean & Modular)
```
project/
├── data/
│   ├── raw/
│   └── processed/
├── notebooks/
│   ├── 01_data_exploration.ipynb
│   ├── 02_model_experimentation.ipynb
│   └── 03_results_analysis.ipynb
├── src/
│   ├── __init__.py
│   ├── data_preprocessing.py
│   ├── model.py
│   ├── train.py
│   ├── evaluate.py
│   ├── inference.py
│   └── utils.py
├── pipelines/
│   ├── train_pipeline.py
│   ├── inference_pipeline.py
│   └── evaluation_pipeline.py
├── configs/
│   ├── model_config.yaml
│   ├── train_config.yaml
│   └── inference_config.yaml
├── experiments/
│   └── logs/
├── models/
│   └── checkpoints/
├── requirements.txt
├── README.md
└── run_demo.py
```

### 2. Documentation & Notebooks
- **README.md**: Setup instructions, usage examples, design decisions
- **Jupyter Notebooks**: 
  - Data exploration with visualizations and insights
  - Model experimentation and hyperparameter analysis
  - Results analysis with error examples and improvement suggestions
- **Code comments**: Clear docstrings and inline comments
- **Results summary**: Model performance, key findings

### 3. Executable Pipelines
Create command-line interfaces for each major workflow:

**Training Pipeline:**
```bash
python pipelines/train_pipeline.py --config configs/train_config.yaml
# Should handle: data loading → preprocessing → training → validation → model saving
```

**Inference Pipeline:**
```bash
python pipelines/inference_pipeline.py --model_path models/best_model.pt --input_file data/test_samples.csv
# Should handle: model loading → preprocessing → batch prediction → output saving
```

**Evaluation Pipeline:**
```bash
python pipelines/evaluation_pipeline.py --model_path models/best_model.pt --test_data data/test.csv
# Should handle: model loading → evaluation → metrics calculation → report generation
```

### 3. Live Dem
- **Pipeline Demonstration**: Show training, inference, and evaluation pipelines in action
- **Notebook Walkthrough**: Key insights from data exploration and experimentation
- **Code Architecture**: Explain design choices and component interactions
- **Performance Analysis**: Model results, error analysis, improvement ideas
- **Q&A**: Discuss trade-offs, improvements, production considerations

---

## 🎯 Evaluation Criteria

### Technical Skills
- **Code Quality**: Clean, modular, well-documented code
- **ML Implementation**: Proper fine-tuning, evaluation, metrics
- **Data Handling**: Preprocessing, splitting, batching
- **Pipeline Design**: Executable, configurable, and robust workflows
- **Notebook Quality**: Clear analysis, insights, and experimentation

### System Design
- **Architecture**: Logical code organization and separation of concerns
- **Pipeline Integration**: Seamless flow between training, inference, and evaluation
- **Configurability**: Easy to modify hyperparameters and model choices
- **Reproducibility**: Consistent results across runs
- **Best Practices**: Following ML engineering conventions

### Problem Solving
- **Dataset Analysis**: Understanding data characteristics and challenges
- **Model Choice**: Justified selection of model and approach
- **Performance Optimization**: Addressing class imbalance, overfitting, etc.
- **Trade-off Awareness**: Understanding of speed vs accuracy, etc.

### Communication
- **Documentation**: Clear README and code documentation
- **Problem Articulation**: Clear explanation of challenges and solutions

-

---

## 🛠️ Suggested Tech Stack
**Required:**
- PyTorch or TensorFlow/Keras
- HuggingFace Transformers
- pandas, numpy, scikit-learn
- matplotlib/seaborn for visualization
- FastAPI for inference API



## 🔧 Pipeline Requirements

### Training Pipeline (`pipelines/train_pipeline.py`)
**Must include:**
```python
# Key components your training pipeline should handle:
- Config loading and validation
- Data loading and preprocessing
- Model initialization
- Training loop with logging
- Validation and early stopping
- Model checkpointing and saving
- Experiment metadata logging
```

**Usage:**
```bash
python pipelines/train_pipeline.py --config configs/train_config.yaml --experiment_name "baseline_bert"
```

### Inference Pipeline (`pipelines/inference_pipeline.py`)
**Must include:**
```python
# Key components your inference pipeline should handle:
- Model loading from checkpoint
- Input data preprocessing
- Batch or single prediction
- Confidence score calculation
- Output formatting and saving
- Error handling for malformed inputs
```

**Usage:**
```bash
# Single prediction
python pipelines/inference_pipeline.py --model_path models/best_model.pt --text "I want to return my order"

# Batch prediction
python pipelines/inference_pipeline.py --model_path models/best_model.pt --input_file data/new_queries.csv --output_file results/predictions.csv
```

### Evaluation Pipeline (`pipelines/evaluation_pipeline.py`)
**Must include:**
```python
# Key components your evaluation pipeline should handle:
- Model loading from checkpoint
- Test data loading and preprocessing
- Comprehensive evaluation metrics
- Confusion matrix and classification report
- Error analysis with examples
- Results saving and visualization
```

**Usage:**
```bash
python pipelines/evaluation_pipeline.py --model_path models/best_model.pt --test_data data/test.csv --output_dir results/evaluation/
```


## ⚡ Success Indicators
- All three pipelines execute successfully without errors
- Jupyter notebooks show clear data insights and experimentation
- Model achieves >85% accuracy on test set
- Clean, production-ready code structure with proper separation of concerns
- Comprehensive evaluation with actionable insights
- Clear demonstration of ML engineering best practices
- Ability to articulate technical decisions confidently during demo