The real value of a modern data platform is realized only when business users and applications are able to access raw and aggregated data from a range of sources, and produce data-driven insights in a timely manner. And with Machine Learning, analysts and data scientists can leverage historical data to help make better, data-driven business decisions—offline and in real-time using technologies such as TensorFlow.

In this talk, you will learn how to train a simple neural network TensorFlow model in Python and use it as a dataflow pipeline created in the open source StreamSets Data Collector. The dataflow pipeline will ingest breast cancer data and classify cancer conditions as being benign or malignant (using the trained and saved TF model) within a contained environment—without having to initiate HTTP or REST API calls to ML models served and exposed as web services.