Model deployment refers to making a trained ML model accessible and usable in a real-world production environment by integrating it into a production system and monitoring its performance. The goal of model deployment is to ensure that the ML model can be used to make accurate predictions on new data in a reliable, scalable, and efficient manner.
The steps involved in deploying an ML model can vary but typically include:
- ML model packaging: The ML model is packaged in a format easily integrated into the production system, such as a REST API or Python package.
- Integration with production system: The packaged ML model is integrated into the production system to receive input data and produce predictions.
- Performance monitoring: ML model is monitored in real-world conditions to ensure that it is making accurate predictions and functioning correctly.
- Maintaining and updating: The deployed ML model may require updates and maintenance over time to address issues such as model drift, changes in the data distribution, or changes in the underlying technology.