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Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM)

 

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.

Contenu

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
  • CloudSQL: 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

A qui s'adresse cette formation

This class is intended for the following participants:

  • 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

Pré-requis

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

Objectifs

This course teaches participants the following skills:

  • 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: Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM)

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
  • CloudSQL: 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
Formation en ligne

Durée 1 jour

Prix
  • CAD 785,-
Classroom training

Durée 1 jour

Prix
  • Canada: CAD 785,-
 
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This is an Instructor-Led Classroom course
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