11 Machine learning in Pulsar
This chapter covers
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Exploring how Pulsar Functions can be used to provide near real-time machine learning
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Developing and maintaining the collection of inputs required by the machine learning model to provide a prediction
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Executing any PMML-supported model inside a Pulsar function
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Executing non-PMML models inside a Pulsar function
One of the primary goals of machine learning is the extraction of actionable insights from raw data. Having insights that are actionable means you can use them to make strategic, data-driven decisions that result in a positive outcome for your business and customers. For instance, every time a customer places an order on the GottaEat application, we want to be able to provide the ...
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