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Google Cloud Solutions II: Data and Machine Learning

8 Labs · 58 Credits · 7h 45m

Use Case (Experienced) Solutions data and machine learning

In this Quest, you will learn common solution patterns compiled by Google's own Solutions Architects. Each hands-on experience presents an application of multiple Google Cloud services to accomplish a common technical use case. These labs are mostly at the Advanced or Expert level, and students should have earned at least 2-3 badges prior to attempting these labs. Suggested prerequisites: GCP Essentials Quest, Data Engineering Quest, and the Scientific Data Processing Quest.

Exploring NCAA Data with BigQuery

In this lab, we will use BigQuery to explore the NCAA dataset of basketball games, teams, and players. The game data covers play-by-play and box scores back to 2009, as well as final scores back to 1996.

Icon  fundamental fundamental 5 Credits 45 Minutes

Creating Custom Interactive Dashboards with Bokeh and BigQuery

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.

Icon  expert expert 9 Credits 1 Hour

Running R at Scale on Google Compute Engine

Deploy and test an R cluster using ElastiCluster, Snow, RHIPE, Rslurm and rmpi with Google Compute Engine.

Icon  expert expert 9 Credits 1 Hour

Creating an Object Detection Application Using TensorFlow

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.

Icon  fundamental fundamental 5 Credits 45 Minutes

Running Distributed TensorFlow on Compute Engine

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.

Icon  expert expert 9 Credits 1 Hour 30 Minutes

Using Distributed TensorFlow with Cloud ML Engine and Cloud Datalab

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.

Icon  advanced advanced 7 Credits 45 Minutes

Using OpenTSDB to Monitor Time-Series Data on Cloud Platform

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.

Icon  advanced advanced 7 Credits 1 Hour

Scanning User-generated Content Using the Cloud Video Intelligence and Cloud Vision APIs

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.

Icon  advanced advanced 7 Credits 1 Hour