Summary

In this chapter, we have covered an overview of what TensorFlow is and how it serves as an improvement over earlier frameworks for deep learning research. We also explored setting up an IDE, VSCode, and the foundation of reproducible applications, Docker containers. To orchestrate and deploy Docker containers, we discussed the Kubernetes framework, and how we can scale groups of containers using its API. Finally, I described Kubeflow, a machine learning framework built on Kubernetes which allows us to run end-to-end pipelines, distributed training, and parameter search, and serve trained models. We then set up a Kubeflow deployment using Terraform, an IaaS technology.

Before jumping into specific projects, we will next cover the basics of neural network theory and the TensorFlow and Keras commands that you will need to write basic training jobs on Kubeflow.