Google Cloud Fundamentals: Big Data & Machine Learning

Course Outline

The course includes presentations, demonstrations and hands-on labs.

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