continuous training

Continuous training, also known as continuous machine learning (CML), is a process where a machine learning model is updated continually with new data to adapt to changes over time. Continuously training the machine learning models with new data ensures they remain accurate and relevant. This is different from traditional machine learning, where a model is trained once and then deployed without further updates unless manually retrained.

Continuous training is essential in environments where data is rapidly changing, and models need to stay current to maintain accuracy and reliability. For example, machine learning models designed to protect against cyberattacks need to continuously update their models with real-time data [2][4][6].

Continuous training is also useful In autonomous vehicles and robotics, as it allows systems to adapt to new environments and scenarios, improving their decision-making and performance over time. This impacts many industries, including warehousing, transportation, and manufacturing.

Citations:

[1] https://www.datacamp.com/blog/what-is-continuous-learning

[2] https://levity.ai/blog/what-is-continuous-machine-learning

[3] https://www.algolia.com/blog/ai/how-continuous-learning-lets-machine-learning-provide-increasingly-accurate-predictions-and-recommendations/

[4] https://neptune.ai/blog/retraining-model-during-deployment-continuous-training-continuous-testing

[5] https://www.reddit.com/r/singularity/comments/14o1cc0/can_an_ml_model_be_continuously_updated_with_new/

[6] https://www.techtarget.com/searchenterpriseai/tip/Why-continuous-training-is-essential-in-MLOps

[7] https://ai.stackexchange.com/questions/3920/what-do-you-call-a-machine-learning-system-that-keeps-on-learning

[8] https://towardsdatascience.com/how-to-apply-continual-learning-to-your-machine-learning-models-4754adcd7f7f