In this advanced-level quest, you will learn how to harness serious Google Cloud 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 Google Cloud is the go-to platform for running big data and machine learning jobs.
Overview
Syllabus
- 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.
- Classify Images of Cats and Dogs using Transfer Learning
- In this lab, you will learn how to classify images of cats and dogs by using transfer learning from a pre-trained network.
- 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.
- 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.
- 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.
