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Google Cloud Solutions II: Data and Machine Learning

Expert · 10 Labs · 70 Credits · 10h

Use Case (Experienced) Solutions data and machine learning

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.

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Exploring NCAA Data with BigQuery

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.

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 1 Hour

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

Introduction to Kubeflow on Google Kubernetes Engine

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.

Icon  advanced advanced 7 Credits 1 Hour

TensorFlow for Poets

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.

Icon  fundamental fundamental 5 Credits 1 Hour

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