TensorFlow is a new Open Source framework created at Google for building Deep Learning applications. I will discuss how it compares to other Python machine learning libraries like Theano or Chainer. Finally, I will discuss how trained TensorFlow models could be deployed into a production system using TensorFlow Serve.

The audience of this talk are DevOps engineers, Developers, and System Administrators who want to learn the latest information about managing and automating in containerized environments. Attendees will learn how container cluster managers like Kubernetes work, and how to interact with the Kubernetes API specifically from Python.

Python has lots of scientific, data analysis, and machine learning libraries. But there are many problems. Which do you use? How do they compare to each other? How can you use a model that has been trained in your production application? TensorFlow is a new Open Source framework created at Google for building Deep Learning applications. Tensorflow allows you to construct easy to understand data flow graphs which form a mathematical and logical pipeline. Creating data flow graphs allow easier visualization of complicated algorithms as well as running the training operations over multiple hardware GPUs. Tensorflow data flow graphs and operations are written in Python. In this talk I will discuss how you can use TensorFlow to create Deep Learning applications. I will discuss how it compares to other Python machine learning libraries like Theano or Chainer. Finally, I will discuss how trained TensorFlow models could be deployed into a production system using TensorFlow Serve.