Posttraining

Posttraining is a term used to describe activities and processes that occur after a training period or program. It is used as an adjective or compound noun in several fields, including education, workforce development, and machine learning.

In education and professional development, posttraining activities typically include assessment of learning outcomes, certification or credentialing, and plans to transfer skills to the workplace. They may also involve refresher courses, coaching, or follow-up evaluations to reinforce new competencies and determine next steps for learners or organizations.

In the field of machine learning, post-training refers to actions performed after a model has completed its initial training. Common post-training steps include calibration of predicted probabilities, quantization or pruning for deployment efficiency, and optional fine-tuning with additional data. Post-training evaluation on new data and drift monitoring may also be performed to ensure robustness in production.

Posttraining activities are distinct from pretraining and fine-tuning, and they focus on applying, validating, and maintaining what was learned during the training phase. Effective posttraining processes contribute to reliability, scalability, and alignment with real-world requirements, but they can introduce additional resource needs and risk of performance drift if not managed carefully.

See also: transfer learning, continuing education, model deployment, model monitoring.