TensorFlow has emerged as one of the leading machine learning libraries, and when combined with an operational database, it provides the foundation for quickly building sophisticated machine learning workflows.In this post, we will explore a machine learning workflow using a speed dating dataset.

The overall objective of this demonstration is to compare the machine-suggested matches with those  a person might choose directly from looking at different people’s profiles.

The dataset comes from a speed dating experiment on Kaggle.As part of the workflow, we will detail how you can use MemSQL Pipelines to stream data from Kafka in real time into the database. Upon ingesting the data, we will incorporate TensorFlow to train and classify data simultaneously using some of the built-in TensorFlow algorithms.

Finally, we’ll see how well the machine determines matches.To read this article in full, please click here

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