When does a deployed Machine Learning model fail? There could be some good answers. One among them is when a model is deployed to make predictions on unforeseen data whose pattern is entirely different from training data. This issue is common in dynamic scenarios where non-stationary data streams in continuously. Continual Learning (CL) is a real-time machine learning approach that tries to solve dynamically varying data patterns. While making predictions on incoming unforeseen data, a CL model uses the same data and the feedback on its predictions (whether correct or incorrect) to train itself continuously in real-time.

Read more: https://analyticsindiamag.com/avalanche-a-python-library-for-continual-learning/

#machine-learning #python #opensource #pytorch