In Python machine learning, how do I handle configuration and secrets?

Handling configuration and secrets in Python machine learning projects is essential for maintaining security and flexibility. This includes securely storing API keys, database credentials, and other sensitive information, while also allowing dynamic configuration of your ML models and workflows.
configuration management, secrets management, Python machine learning, secure storage, environment variables
# Example of handling configuration and secrets in a Python project import os from dotenv import load_dotenv # Load environment variables from a .env file load_dotenv() # Access sensitive information using environment variables API_KEY = os.getenv("API_KEY") DATABASE_URI = os.getenv("DATABASE_URI") # Example usage in a machine learning function def connect_to_database(uri): # Connection logic here pass if __name__ == "__main__": connect_to_database(DATABASE_URI)

configuration management secrets management Python machine learning secure storage environment variables

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?