Caching

Sometimes, our machine learning algorithms will be trained by and/or given input for prediction via data from external sources (for example, APIs), that is, data that isn't local to the application running our modeling or analysis. Further, we might have various sets of data that are being accessed frequently, may be accessed again soon, or may need to be made available while the application is running.

In at least some of these cases, it might make sense to cache data in memory or embed the data locally where the application is running. For example, if you are reaching out to a government API (typically having high latency) for census data frequently, you may consider maintaining a local or in-memory cache of the census data being used so that you can avoid constantly reaching out to the API.