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Streamlit Tutorial - 13 | Caching - st.cache_data , st.cache_resource

In this video, we are going to look into the caching functions of streamlit. Specifically, we are going to deal with st.cache_data and st.cache_resource. Both the caching functions do one things i.e. they do not let the function associated with them run if two conditions are satisfied. This feature is primarily useful as streamlit programs rerun often is some event takes place on the front end. The first conditions is that the arguments to the functions do not change and second that the code inside the functions does not change. st.cache_resource from the data science perspective is used mostly for loading the model. st.cache_data is used more widely including running inference on the model and/or doing bulky calculations or getting data from the internet. I have slipped the the technical part of how data is stored in st.cache_resource and st.cache_data as they were out of the scope of the video. Code: https://github.com/DatumLearning/Stre... Instagram:   / datumlearning