Lifecycle management for customized natural language processing

A method, comprising:

• determining, by a natural language processing (NLP) customization service, a task definition associated with an NLP model based at least in part on user input, wherein the task definition comprises an indication of one or more tasks to be implemented using the NLP model and one or more requirements associated with use of the NLP model;
• determining, by the NLP customization service, the NLP model based at least in part on the task definition;
• training, by the NLP customization service, the NLP model;
• performing inference using the NLP model for a plurality of input documents, wherein the inference outputs a plurality of predictions based at least in part on the input documents, and wherein inference data is collected based at least in part on the inference;
• and
• generating, by the NLP customization service, a retrained NLP model based at least in part on the inference data.

One or more non-transitory computer-readable storage media storing program instructions that, when executed on or across one or more processors, perform:

• determining, by a natural language processing (NLP) customization service, a task definition associated with an NLP model based at least in part on user input, wherein the task definition comprises an indication of one or more tasks to be implemented using the NLP model and one or more requirements associated with use of the NLP model in a production environment;
• selecting, by the NLP customization service, the NLP model from a plurality of candidate NLP models based at least in part on the task definition;
• training, by the NLP customization service, the NLP model;
• performing inference using the NLP model for a plurality of input documents, wherein the input documents comprise natural language text, wherein the inference outputs a plurality of predictions based at least in part on the natural language text, and wherein inference data is collected based at least in part on the inference;
• generating, by the NLP customization service, a retrained NLP model based at least in part on the inference data;
• and
• performing additional inference using the retrained NLP model for a plurality of additional input documents, wherein additional inference data is collected based at least in part on the additional inference.