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Google Cloud Fundamentals: Big Data and Machine Learning

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1days
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910,00 
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Time: 11:00 - 19:00
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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.

Target Audience

  • 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.

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.

Outline

Module 1: Introducing Google Cloud Platform

  • Google Platform Fundamentals Overview.
  • Google Cloud Platform Data Products and Technology.
  • Usage scenarios.
  • Lab: Sign up for Google Cloud Platform.

Module 2: Compute and Storage Fundamentals

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

Module 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 SparkML.

Module 4: Scaling Data Analysis

  • Fast random access.
  • Datalab.
  • BigQuery.
  • Lab: Build machine learning dataset.
  • Machine Learning with TensorFlow.
  • Lab: Train and use neural network.
  • Fully built models for common needs.
  • Lab: Employ ML APIs

Module 5: Data Processing Architectures

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

Module 6: Summary

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

Prerequisites

To get the most of out of this course, participants should have:

  • 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.

 

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