Google Cloud Fundamentals:˜ Big Data and Machine Learning

  • Data analysts getting started with Google Cloud Platform
  • Data scientists getting started with Google Cloud Platform
  • 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

Prerequisite

  • 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

Expected Duration
1 day

Description

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, you will 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.

Objective

1. Introducing Google Cloud Platform

  • Google Platform Fundamentals Overview
  • Google Cloud Platform Data Products and Technology
  • Usage scenarios

2. Compute and Storage Fundamentals

  • CPUs on demand (Compute Engine)
  • A global filesystem (Cloud Storage)
  • CloudShell

3. Data Analytics on the Cloud

  • Stepping-stones to the cloud
  • CloudSQL: your SQL database on the cloud
  • Lab: Importing data into CloudSQL and running queries
  • Spark on Dataproc

4. Scaling Data Analysis

  • Fast random access
  • Datalab
  • BigQuery
  • Machine Learning with TensorFlow
  • Fully built models for common needs

5. Data Processing Architectures

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

6. Summary

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

SUBSCRIPTION COST


$599.00

 

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