Deploying an NLP model
Deploying an NLP model requires selecting a deployment method that can handle the expected inference load during peak production while being efficient and secure. During the testing phase, the model can often be run locally if it is small enough. However, a production use case necessitates a production deployment strategy. The model can be deployed on virtual machines, through a cloud provider service, on a dedicated model hosting service such as Hugging Face, or, when possible, within the same service where the vectors will be used, such as Elasticsearch. Each method has its trade-offs between efficiency, latency, scaling, and management.
Interaction with external deployment methods typically involves using APIs to send text inputs and receive vectors as outputs. Regardless of how the vectors are generated, they are sent to a data store for storage and can later be searched against using exact match or approximate nearest neighbor search. Vectors generated...
