Example Concept: Common challenges in deploying machine learning models include data drift, where the data distribution changes over time, leading to degraded model performance; scalability issues, as models need to handle increased loads efficiently; integration with existing systems, which requires compatibility and seamless operation; monitoring and maintenance, to ensure models continue to perform well and adapt to new data; and security concerns, to protect data privacy and model integrity.