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.
Imply Lumi Loglake Demo
In this demo, you'll see exactly how teams are using Lumi to do more with Splunk and modern observability stacks — without replatforming. We'll also give you a first look at Lumi Loglake — our newest feature...
Watch nowImply Lumi Observability Warehouse Demo
In this 30-minute session, you'll see a demo of Imply Lumi — the observability data layer built to help you store more, search faster, and reduce cost without changing your existing tools.
Watch nowLunch & Learn: Imply Lumi Observability Warehouse Demo
In this 30-minute session, you'll see a demo of Imply Lumi — the observability data layer built to help you store more, search faster, and reduce cost without changing your existing tools.
Watch now