Mini toolsDefinition
The process of making machine learning models available for use in a production environment.
Detailed Explanation
Model deployment involves integrating trained ML models into production systems where they can receive inputs and return predictions. This includes model serialization, environment configuration, scaling considerations, monitoring setup, and establishing deployment strategies. The process must ensure reliability, performance, and maintainability while managing computational resources effectively and handling real-world data requirements.
Use Cases
Real-time recommendation systems Automated fraud detection services Production-scale image recognition systems Customer service chatbots
