In this advanced-level quest, you will learn how to harness serious GCP computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why GCP is the go-to platform for running big data and machine learning jobs.
In this hands-on lab, you will install Kubeflow on an empty Kubernetes Engine cluster and use it to train and serve a sequence-to-sequence model using TensorFlow, Keras, and SeldonIO.
This lab will serve as an introduction to Kubeflow, an open-source project which aims to make running ML workloads on Kubernetes simple, portable and scalable.