modellserving

Model serving refers to the process of deploying trained machine learning models into a production environment where they can be accessed to make predictions on new, unseen data. This is a crucial step in operationalizing machine learning, transforming a developed model from a research artifact into a business asset. The primary goal of model serving is to make the model available in a reliable, scalable, and efficient manner.

There are several common approaches to model serving. One popular method is to expose the model via a REST API. This allows applications to send data to the model endpoint and receive predictions in return, typically in a format like JSON. Another approach involves batch prediction, where large datasets are processed offline to generate predictions, which are then stored for later use. Real-time inference, on the other hand, focuses on generating predictions with very low latency, often for interactive applications.

Key considerations in model serving include performance, scalability, reliability, and monitoring. Performance is measured by factors like prediction latency and throughput. Scalability ensures that the serving infrastructure can handle varying loads of prediction requests. Reliability is paramount, as system failures can lead to missed predictions and service disruptions. Monitoring involves tracking model performance in production, detecting drift, and ensuring the system is operating as expected. Various tools and platforms exist to facilitate model serving, ranging from cloud-based services to on-premises solutions.