You should update an algorithm when the underlying data source has been changed or whenever there’s a case of non-stationarity. The algorithm should also be updated when you want the model to evolve as data streams through the infrastructure.
The correct answer to the question “When is it necessary to update an algorithm?” would be:
“An algorithm should be updated whenever there are changes in the underlying data distribution, objectives, or constraints that the algorithm addresses. Additionally, updates may be necessary when new research or advancements in the field offer improvements or optimizations. Regular monitoring and evaluation of the algorithm’s performance can also reveal areas for enhancement or adaptation to evolving circumstances. Ultimately, the decision to update an algorithm should be driven by the need to maintain its relevance, efficiency, and effectiveness in solving the problem it was designed for.”