An intense presentation designed to operationalize machine learning. This talk focuses on dividing specializations; data engineer and data scientist.

The data engineer ensures that data is delivered, manipulated, and harnessed. The data engineer does this to be useful for the data scientist. The data engineer is also versant in Java and Scala and will be knowledgeable in pub-subs like Kafka.

The data scientist uses that data, does their cleaning, and investigates possible patterns designing a machine learning model that we can use to either find regressions or classifications for our data. The data scientists use Python, Jupyter Notebooks, Tensorflow, and Matplotlib as their tools of choice for constructing a machine-learning model to make decisions about the data.

This presentation answers the question. How do we take that model and tie it to everything else? This workshop will use a wide array of technologies. It will set you on the path to running Machine Learning Pipelines in Kubernetes using Kafka and Tensorflow, so you can start immediately when you return to work.