Machine learning models require thousands of data points to train models. This session will look at a number of mechanisms druid offers to speed up model training. The tuple sketch can be used to train models where many different metrics are required for training. The sessionization extension can be used to speed up training for use cases where the need is to forecast viewership or session experience. Druid’s ability to output metrics at different time granularities can be used to smoothen out time forecasts.
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