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    Google Cloud Fundamentals: Big Data and Machine Learning

    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