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

Detailed Course 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
  • 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