retrening

Retrening is not a widely used term in English and is typically a misspelling or neologism of retraining. When encountered, it generally refers to the process of teaching again or updating capabilities after an initial training phase, across human or technical contexts.

In professional and educational settings, retraining denotes programs designed to refresh or expand employees’ skills to meet evolving job requirements, comply with new regulations, or adopt new technologies. Such programs may focus on safety standards, software tools, or industry processes and are often part of workforce development, career planning, and organizational change initiatives.

In technical fields, especially in artificial intelligence and data science, retraining describes updating a system or model with new data to preserve performance, adapt to changing conditions, or enable new tasks. Common approaches include incremental or continual learning, fine-tuning a pre-trained model on recent labeled data, or re-training on refreshed datasets. Challenges in this area include data quality and labeling costs, computational resources, and the risk of catastrophic forgetting of previously learned information.

Applications of retraining span multiple sectors, including manufacturing, finance, healthcare, and customer analytics. Effectiveness depends on data governance, alignment with goals, and ongoing evaluation. Ethical and practical considerations involve privacy, transparency, accessibility, and ensuring that retraining processes do not reinforce biases or inequities.

See also: retraining, lifelong learning, upskilling, continuous education, machine learning.