# Condensed: Predicting Columns in a Table - Deployment Optimization

Summary: This tutorial demonstrates AutoGluon model deployment optimization techniques, focusing on efficient model cloning and performance enhancement for production environments. It covers implementation of basic model training, two types of predictor cloning (standard and deployment-optimized), and experimental model compilation for faster inference. Key functionalities include using clone_for_deployment() to minimize artifact size, persist() for memory-based acceleration, and model compilation for specific model types. The tutorial helps with tasks like optimizing model deployment size, improving prediction speed, and managing version compatibility, while highlighting important considerations for storage management and functionality trade-offs in production settings.

*This is a condensed version that preserves essential implementation details and context.*

Here's the condensed tutorial focusing on key implementation details and best practices:

# AutoGluon Deployment Optimization Guide

## Key Implementation Steps

### 1. Basic Model Training
```python
from autogluon.tabular import TabularDataset, TabularPredictor

# Train initial predictor
save_path = 'agModels-predictClass-deployment'
predictor = TabularPredictor(label=label, path=save_path).fit(train_data)
```

### 2. Predictor Cloning

#### Standard Clone
```python
# Create exact replica of predictor
save_path_clone = save_path + '-clone'
path_clone = predictor.clone(path=save_path_clone)
predictor_clone = TabularPredictor.load(path=path_clone)
```

#### Deployment-Optimized Clone
```python
# Create minimal version for deployment
save_path_clone_opt = save_path + '-clone-opt'
path_clone_opt = predictor.clone_for_deployment(path=save_path_clone_opt)
predictor_clone_opt = TabularPredictor.load(path=path_clone_opt)

# Persist model in memory for faster predictions
predictor_clone_opt.persist()
```

### 3. Model Compilation (Experimental)
```python
# Compile for faster inference (requires skl2onnx and onnxruntime)
predictor_clone_opt.compile()
```

## Critical Configurations & Best Practices

1. **Deployment Optimization**:
   - Use `clone_for_deployment()` for production to minimize artifact size
   - Call `persist()` to keep model in memory for faster predictions

2. **Version Compatibility**:
   - Use same Python version for training and inference
   - Maintain consistent AutoGluon versions

3. **Model Compilation**:
   - Only works with RandomForest and TabularNeuralNetwork models
   - Requires additional packages: `autogluon.tabular[skl2onnx]`
   - Clone before compilation as compiled models can't be retrained

4. **Storage Considerations**:
   - Standard clone doubles disk usage
   - Deployment-optimized clone significantly reduces size
   - Store deployment artifacts in centralized storage (e.g., S3)

## Important Notes

- Optimized clones have limited functionality (mainly prediction)
- Compilation may slightly affect prediction results
- Always maintain original predictor as backup before modifications
- Use `predictor.disk_usage()` to monitor storage requirements
- Compiled models provide faster inference but lose training capability