Google Cloud Solutions II: Data and Machine Learning
Expert 10 Steps 10h 10m 70 Credits
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.
PrerequisitesThis Quest expects solid hands-on proficiency with Google Cloud workflows and processes, especially those involving multiple services working together. It is recommended that the student have at least earned a Badge by completing the hands-on labs in the Quest. Additional experience with the labs in the Machine Learning APIs Quest will also be useful.
Use BigQuery to explore the NCAA dataset of basketball games, teams, and players. The data covers plays from 2009 and scores from 1996. Watch How the NCAA is using Google Cloud to tap into decades of sports data.
In this lab you will learn how to install and run TensorFlow on a single machine, then train a simple classifier to classify images of flowers.
This lab will show you how to install and run an object detection application. The application uses TensorFlow and other public API libraries to detect multiple objects in an uploaded image.
Deploy and test an R cluster using ElastiCluster, Snow, RHIPE, Rslurm and rmpi with Google Compute Engine.
In this lab you will learn how to build a custom interactive dashboard application on Google Cloud Platform (GCP) by using the Bokeh library to visualize data from publicly available Google BigQuery datasets.
This lab shows you how to use a distributed configuration of TensorFlow on multiple Compute Engine instances to train a convolutional neural network model using the MNIST dataset.
This lab shows you how to use a distributed configuration of TensorFlow code in Python on Google Cloud Machine Learning Engine to train a convolutional neural network model by using the MNIST dataset.
In this lab you will learn how to collect, record, and monitor time-series data on Google Cloud Platform (GCP) using OpenTSDB running on Google Kubernetes Engine and Google Cloud Bigtable.
This lab will show you how to deploy a set of Cloud Functions in order to process images and videos with the Cloud Vision API and Cloud Video Intelligence API.
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.