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Google Cloud Solutions II: Data and Machine Learning

date_range 5時間 show_chart Expert universal_currency_alt クレジット: 29

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.

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Enroll in this on-demand quest
  • ラボ

    Exploring NCAA Data with BigQuery

    BigQuery を使用して、NCAA のバスケットボールの試合、チーム、選手に関するデータセットを探索します。データは 2009 年以降のプレイと 1996 以降のスコアをカバーしています。NCAA が Google Cloud を使用して数十年分のスポーツのデータを活用する方法をご覧ください。

  • ラボ

    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.

  • ラボ

    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.

  • ラボ

    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.

  • ラボ

    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.

  • info
    Quest Info
    Prerequisites
    This 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.