How do you ensure the scalability of Machine Learning models in large-scale applications?

Scalability of Machine Learning models in large applications requires the implementation of some sort of parallel computation in large-sized data, due to the need to perform distributed computing. To this end, one can apply load balancing and autoscaling strategies in case of demand spikes.

Further scaling of the model is possible by optimization of the model architecture itself, where one is able to go on cloud services that allow such a model to process huge amounts of data with high efficiency across multi-environments.