Debugging and Error Handling

Duration: 8 min

Master debugging techniques and robust error handling for production AI systems.

Using the Python Debugger

The pdb module lets you step through code and inspect variables.

import pdb

def train_model(data):
    pdb.set_trace()  # Execution pauses here
    processed = [x * 2 for x in data]
    return sum(processed)

# In debugger:
# n - next line
# s - step into function
# c - continue
# p variable - print variable
# l - list code

Try it in Google Colab:

Custom Exception Handling

Create specific exceptions for better error handling in AI pipelines.

class DataValidationError(Exception):
    """Raised when data validation fails"""
    pass

class ModelNotFoundError(Exception):
    """Raised when model file doesn't exist"""
    pass

def validate_data(data):
    if not data or len(data) == 0:
        raise DataValidationError("Data cannot be empty")
    return data

try:
    validate_data([])
except DataValidationError as e:
    print(f"Validation failed: {e}")

Validation failed: Data cannot be empty

💡 Tip: Use specific exception types to make error handling more precise and informative.