Machine Learning Data Pipelines with Kafka and TensorFlow

65 Minutes

Our titles are shifting between data engineer and data scientist. 

The data engineer ensures that data is delivered, manipulated, and harnessed. The data engineer does this so that it is useful for the data scientist. The data engineer is conversant in Java and Scala.

The data scientist uses that data, 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.

So how do we bring them together and how do we work together? This presentation by Daniel Hinojosa covers one possible solution set: Kafka, Kafka Streams, Tensor Flow, and Kubernetes.

Speaker Daniel Hinojosa

Daniel is a programmer, consultant, instructor, speaker, and recent author. With over 20 years of experience, he does work for private, educational, and government institutions. He is also currently a speaker for the No Fluff Just Stuff tour. Daniel loves JVM languages like Java, Groovy, and Scala, but also dabbles with non JVM languages like Haskell, Ruby, Python, LISP, C, C++. He is an avid Pomodoro Technique Practitioner and makes every attempt to learn a new programming language every year. For downtime, he enjoys reading, swimming, Legos, football, and barbecuing.

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