Google Cloud Fundamentals: Big Data and Machine Learning


1 Day


Delivery Methods
Virtual Instructor Led
Private Group

Course Overview

This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.

Course Objectives

  • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.
  • Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.
  • Employ BigQuery and Cloud Datalab to carry out interactive data analysis.
  • Train and use a neural network using TensorFlow.
  • Employ ML APIs.
  • Choose between different data processing products on the Google Cloud Platform.
  • Who Should Attend?

  • Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform.
  • Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports.
  • Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists.
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    Course Prerequisites

  • Basic proficiency with common query language such as SQL.
  • Experience with data modeling, extract, transform, load activities.
  • Developing applications using a common programming language such Python.
  • Familiarity with machine learning and/or statistics.
  • Agenda

    1 - Introducing Google Cloud Platform

    • Google Platform Fundamentals Overview.
    • Google Cloud Platform Big Data Products.

    2 - Compute and Storage Fundamentals

    • CPUs on demand (Compute Engine).
    • A global filesystem (Cloud Storage).
    • Cloud Shell.
    • Lab: Set up an Ingest-Transform-Publish data processing pipeline.

    3 - Data Analytics on the Cloud

    • Stepping-stones to the cloud.
    • Cloud SQL: your SQL database on the cloud.
    • Lab: Importing data into CloudSQL and running queries.
    • Spark on Dataproc.
    • Lab: Machine Learning Recommendations with Spark on Dataproc.

    4 - Scaling Data Analysis

    • Fast random access.
    • Datalab.
    • BigQuery.
    • Lab: Build machine learning dataset.

    5 - Machine Learning

    • Machine Learning with TensorFlow.
    • Lab: Carry out ML with TensorFlow
    • Pre-built models for common needs.
    • Lab: Employ ML APIs.

    6 - Data Processing Architectures

    • Message-oriented architectures with Pub/Sub.
    • Creating pipelines with Dataflow.
    • Reference architecture for real-time and batch data processing.

    7 - Summary

    • Why GCP?
    • Where to go from here
    • Additional Resources

    Upcoming Class Dates and Times

    Dec 14
    9:00 AM - 5:00 PM
    ENROLL $595.00

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