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Course curriculum

  • 1
    You, This Course and Us
  • 2
    Introduction
  • 3
    Compute
  • 4
    Storage
  • 5
    Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
  • 6
    Hadoop Pre-reqs and Context
  • 7
    BigTable ~ HBase = Columnar Store
  • 8
    Datastore ~ Document Database
  • 9
    BigQuery ~ Hive ~ OLAP
  • 10
    Dataflow ~ Apache Beam
  • 11
    Dataproc ~ Managed Hadoop
  • 12
    Pub/Sub for Streaming
  • 13
    Datalab ~ Jupyter
  • 14
    TensorFlow and Machine Learning
  • 15
    Regression in TensorFlow
  • 16
    Vision, Translate, NLP and Speech: Trained ML APIs
  • 17
    Virtual Machines and Images
  • 18
    VPCs and Interconnecting Network
  • 19
    Managed Instance Groups and Load Balancing
  • 20
    Ops and Security
  • 21
    Appendix: Hadoop Ecosystem
  • You, This Course and Us Course Materials Downloads
  • Theory, Practice and Tests Lab: Setting Up A GCP Account Lab: Using The Cloud Shell Important! Delete unused GCP projects/instances
  • About this section Compute Options Google Compute Engine (GCE) Lab: Creating a VM Instance More GCE Lab: Editing a VM Instance Lab: Creating a VM Instance Using The Command Line Lab: Creating And Attaching A Persistent Disk Google Container Engine - Kubernetes (GKE) More GKE Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container App Engine Contrasting App Engine, Compute Engine and Container Engine Lab: Deploy And Run An App Engine App Quiz 1: Compute
  • About this section Storage Options Quick Take Cloud Storage Lab: Working With Cloud Storage Buckets Lab: Bucket And Object Permissions Lab: Life cycle Management On Buckets Fix for AccessDeniedException: 403 Insufficient Permission Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage Transfer Service Lab: Migrating Data Using The Transfer Service gcloud init Lab: Cloud Storage ACLs and API access with Service Account Lab: Cloud Storage Customer-Supplied Encryption Keys and Life-Cycle Management Lab: Cloud Storage Versioning, Directory Sync
  • About this section Cloud SQL Lab: Creating A Cloud SQL Instance Lab: Running Commands On Cloud SQL Instance Lab: Bulk Loading Data Into Cloud SQL Tables Cloud Spanner More Cloud Spanner Lab: Working With Cloud Spanner Important! Delete unused GCP projects/instances
  • Hadoop Pre-reqs and Context
  • About this section BigTable Intro Columnar Store Denormalised Column Families BigTable Performance Lab: BigTable demo Getting the HBase Prompt Important! Delete unused GCP projects/instances
  • About this section Datastore Lab: Datastore demo
  • About this section BigQuery Intro BigQuery Advanced Lab: Loading CSV Data Into Big Query Lab: Running Queries On Big Query Lab: Loading JSON Data With Nested Tables Lab: Public Datasets In Big Query Lab: Using Big Query Via The Command Line Lab: Aggregations And Conditionals In Aggregations Lab: Subqueries And Joins Lab: Regular Expressions In Legacy SQL Lab: Using The With Statement For SubQueries
  • About this section Data Flow Intro Apache Beam Lab: Running A Python Data flow Program Lab: Running A Java Data flow Program Lab: Implementing Word Count In Dataflow Java Lab: Executing The Word Count Dataflow Lab: Executing MapReduce In Dataflow In Python Lab: Executing MapReduce In Dataflow In Java Lab: Dataflow With Big Query As Source And Side Inputs Lab: Dataflow With Big Query As Source And Side Inputs 2
  • About this section Data Proc Lab: Creating And Managing A Dataproc Cluster Lab: Creating A Firewall Rule To Access Dataproc Lab: Running A PySpark Job On Dataproc Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc Lab: Submitting A Spark Jar To Dataproc Lab: Working With Dataproc Using The GCloud CLI
  • About this section Pub Sub Lab: Working With Pubsub On The Command Line Lab: Working With PubSub Using The Web Console Lab: Setting Up A Pubsub Publisher Using The Python Library Lab: Setting Up A Pubsub Subscriber Using The Python Library Lab: Publishing Streaming Data Into Pubsub Lab: Reading Streaming Data From PubSub And Writing To BigQuery Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery Lab: Pubsub Source BigQuery Sink
  • About this section Data Lab Lab: Creating And Working On A Datalab Instance Lab: Importing And Exporting Data Using Datalab Lab: Using The Charting API In Datalab
  • About this section Introducing Machine Learning NN Introduced Representation Learning Introducing TF Lab: Simple Math Operations Computation Graph Tensors Lab: Tensors Linear Regression Intro Placeholders and Variables Lab: Placeholders Lab: Variables Lab: Linear Regression with Made-up Data Image Processing Images As Tensors Lab: Reading and Working with Images Lab: Image Transformations Introducing MNIST K-Nearest Neigbors One-hot Notation and L1 Distance Steps in the K-Nearest-Neighbors Implementation Lab: K-Nearest-Neighbors Learning Algorithm Individual Neuron Learning Regression Learning XOR XOR Trained Downloads
  • About this section Lab: Access Data from Yahoo Finance Non TensorFlow Regression Lab: Linear Regression - Setting Up a Baseline Lab: Linear Regression - Setting Up a Baseline Gradient Descent Lab: Linear Regression Lab: Multiple Regression in TensorFlow Logistic Regression Introduced Linear Classification Lab: Logistic Regression - Setting Up a Baseline Logit Softmax Argmax Lab: Logistic Regression Estimators Lab: Linear Regression using Estimators Lab: Logistic Regression using Estimators Downloads
  • About this section Lab: Taxicab Prediction - Setting up the dataset Lab: Taxicab Prediction - Training and Running the model Lab: The Vision, Translate, NLP and Speech API Lab: The Vision API for Label and Landmark Detection
  • About this section Live Migration Machine Types and Billing Sustained Use and Committed Use Discounts Rightsizing Recommendations RAM Disk Images Startup Scripts And Baked Images
  • About this section VPCs And Subnets Global VPCs, Regional Subnets IP Addresses Lab: Working with Static IP Addresses Routes Firewall Rules Lab: Working with Firewalls Lab: Working with Auto Mode and Custom Mode Networks Lab: Bastion Host Cloud VPN Lab: Working with Cloud VPN Cloud Router Lab: Using Cloud Routers for Dynamic Routing Dedicated Interconnect Direct and Carrier Peering Shared VPCs Lab: Shared VPCs VPC Network Peering Lab: VPC Peering Cloud DNS And Legacy Networks Quiz 2: Networking
  • About this section Managed and Unmanaged Instance Groups Types of Load Balancing Overview of HTTP(S) Load Balancing Forwarding Rules Target Proxy and Url Maps Backend Service and Backends Load Distribution and Firewall Rules Lab: HTTP(S) Load Balancing Lab: Content Based Load Balancing SSL Proxy and TCP Proxy Load Balancing Lab: SSL Proxy Load Balancing Network Load Balancing Internal Load Balancing Autoscalers
  • About this section StackDriver StackDriver Logging Lab: Stackdriver Resource Monitoring Lab: Stackdriver Error Reporting and Debugging Lab: Using Deployment Manager Lab: Deployment Manager and Stackdriver Cloud Endpoints Cloud IAM: User accounts, Service accounts, API Credentials Cloud IAM: Roles, Identity-Aware Proxy, Best Practices Lab: Cloud IAM Data Protection
  • Introducing the Hadoop Ecosystem Hadoop HDFS MapReduce Yarn Hive Hive vs. RDBMS HQL vs. SQL OLAP in Hive Windowing Hive Pig More Pig Spark More Spark Streams Intro Microbatches Window Types Quiz 3: Hadoop Ecosystem

Course Description

What will I learn?

  • Deploy Managed Hadoop apps on the Google Cloud
  • Build deep learning models on the cloud using TensorFlow
  • Make informed decisions about Containers, VMs and AppEngine
  • Use big data technologies such as BigTable, Dataflow, Apache Beam and Pub/Sub


About the course

This course is a really comprehensive guide to the Google Cloud Platform - it has ~25 hours of content and ~60 demos.

The Google Cloud Platform is not currently the most popular cloud offering out there - that's AWS of course - but it is possibly the best cloud offering for high-end machine learning applications. That's because TensorFlow, the super-popular deep learning technology is also from Google.


What's covered: 

  • Compute and Storage - AppEngine, Container Enginer (aka Kubernetes) and Compute Engine
  • Big Data and Managed Hadoop - Dataproc, Dataflow, BigTable, BigQuery, Pub/Sub 
  • TensorFlow on the Cloud - what neural networks and deep learning really are, how neurons work and how neural networks are trained.
  • DevOps stuff - StackDriver logging, monitoring, cloud deployment manager
  • Security - Identity and Access Management, Identity-Aware proxying, OAuth, API Keys, service accounts
  • Networking - Virtual Private Clouds, shared VPCs, Load balancing at the network, transport and HTTP layer; VPN, Cloud Interconnect and CDN Interconnect
  • Hadoop Foundations: A quick look at the open-source cousins (Hadoop, Spark, Pig, Hive and HBase)


Who should take the course?

  • Yep! Anyone looking to use the Google Cloud Platform in their organizations
  • Yep! Any one who is interesting in architecting compute, networking, loading balancing and other solutions using the GCP
  • Yep! Any one who wants to deploy serverless analytics and big data solutions on the Google Cloud
  • Yep! Anyone looking to build TensorFlow models and deploy them on the cloud


Pre-requisites & Requirements

  • Basic understanding of technology - superficial exposure to Hadoop is enough