GCP: Complete Google Data Engineer and Cloud Architect Guide [Video]

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GCP: Complete Google Data Engineer and Cloud Architect Guide [Video]

Loonycorn

The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop
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Video Details

ISBN 139781788999519
Course Length22 hours 43 minutes

Video Description

This course is a really comprehensive guide to the Google Cloud Platform - it has ~20 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.

Style and Approach

This course is a really comprehensive guide to the Google Cloud Platform - it has ~20 hours of content and ~60 demos.

Table of Contents

You, This Course and Us
You, This Course and Us
Introduction
Theory, Practice and Tests
Why Cloud?
Hadoop and Distributed Computing
On-premise, Colocation or Cloud?
Introducing the Google Cloud Platform
Lab: Setting Up A GCP Account
Lab: Using The Cloud Shell
Compute Choices
Compute Options
Google Compute Engine (GCE)
More GCE
Lab: Creating a VM Instance
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
Storage
Storage Options
Quick Take
Cloud Storage
Lab: Working With Cloud Storage Buckets
Lab: Bucket And Object Permissions
Lab: Life cycle Management On Buckets
Lab: Running a Program On a VM Instance And Storing Results on Cloud Storage
Transfer Service
Lab: Migrating Data Using the Transfer Service
Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
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
BigTable ~ HBase = Columnar Store.
BigTable Intro
Columnar Store
Denormalised
Column Families
BigTable Performance
Lab: BigTable demo
Datastore ~ Document Database
Datastore
Lab: Datastore demo
BigQuery ~ Hive ~ OLAP
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
Dataflow ~ Apache Beam
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
Dataproc ~ Managed Hadoop
Data Proc
Lab: Creating And Managing A Dataproc Cluster
Lab: Creating A Firewall Rule To Access Dataproc
Lab: Running A PySpark Job OnDataproc
Lab: Running ThePySpark REPL Shell And Pig Scripts On Dataproc
Lab: Submitting A Spark Jar ToDataproc
Lab: Working With Dataproc Using TheGCloud CLI
Pub/Sub for Streaming.
Pub Sub
Lab: Working With Pubsub On The Command Line
Lab: Working WithPubSub 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 IntoPubsub
Lab: Reading Streaming Data FromPubSub And Writing To BigQuery
Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery
Lab: Pubsub Source BigQuery Sink
Datalab ~ Jupyter
Data Lab
Lab: Creating And Working On A Datalab Instance
Lab: Importing And Exporting Data Using Datalab
Lab: Using the Charting API InDatalab
TensorFlow and Machine Learning
Introducing Machine Learning
Representation Learning
NN Introduced
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 as Unsupervised Learning
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
Regression in TensorFlow
Lab: Access Data from Yahoo Finance
Non TensorFlow Regression
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
Vision, Translate, NLP and Speech: Trained ML APIs
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
Networking
Virtual Private Clouds
VPC and Firewalls
XPC or Shared VPC
VPN
Types of Load Balancing
Proxy and Pass-through load balancing
Internal load balancing
Ops and Security
StackDriver
StackDriver Logging
Cloud Deployment Manager
Cloud Endpoints
Security and Service Accounts
OAuth and End-user accounts
Identity and Access Management
Data Protection
Appendix: Hadoop Ecosystem
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

What You Will Learn

  • Deploy Managed Hadoop apps on the Google Cloud
  • Build deep learning models in 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

Authors

Table of Contents

You, This Course and Us
You, This Course and Us
Introduction
Theory, Practice and Tests
Why Cloud?
Hadoop and Distributed Computing
On-premise, Colocation or Cloud?
Introducing the Google Cloud Platform
Lab: Setting Up A GCP Account
Lab: Using The Cloud Shell
Compute Choices
Compute Options
Google Compute Engine (GCE)
More GCE
Lab: Creating a VM Instance
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
Storage
Storage Options
Quick Take
Cloud Storage
Lab: Working With Cloud Storage Buckets
Lab: Bucket And Object Permissions
Lab: Life cycle Management On Buckets
Lab: Running a Program On a VM Instance And Storing Results on Cloud Storage
Transfer Service
Lab: Migrating Data Using the Transfer Service
Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
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
BigTable ~ HBase = Columnar Store.
BigTable Intro
Columnar Store
Denormalised
Column Families
BigTable Performance
Lab: BigTable demo
Datastore ~ Document Database
Datastore
Lab: Datastore demo
BigQuery ~ Hive ~ OLAP
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
Dataflow ~ Apache Beam
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
Dataproc ~ Managed Hadoop
Data Proc
Lab: Creating And Managing A Dataproc Cluster
Lab: Creating A Firewall Rule To Access Dataproc
Lab: Running A PySpark Job OnDataproc
Lab: Running ThePySpark REPL Shell And Pig Scripts On Dataproc
Lab: Submitting A Spark Jar ToDataproc
Lab: Working With Dataproc Using TheGCloud CLI
Pub/Sub for Streaming.
Pub Sub
Lab: Working With Pubsub On The Command Line
Lab: Working WithPubSub 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 IntoPubsub
Lab: Reading Streaming Data FromPubSub And Writing To BigQuery
Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery
Lab: Pubsub Source BigQuery Sink
Datalab ~ Jupyter
Data Lab
Lab: Creating And Working On A Datalab Instance
Lab: Importing And Exporting Data Using Datalab
Lab: Using the Charting API InDatalab
TensorFlow and Machine Learning
Introducing Machine Learning
Representation Learning
NN Introduced
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 as Unsupervised Learning
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
Regression in TensorFlow
Lab: Access Data from Yahoo Finance
Non TensorFlow Regression
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
Vision, Translate, NLP and Speech: Trained ML APIs
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
Networking
Virtual Private Clouds
VPC and Firewalls
XPC or Shared VPC
VPN
Types of Load Balancing
Proxy and Pass-through load balancing
Internal load balancing
Ops and Security
StackDriver
StackDriver Logging
Cloud Deployment Manager
Cloud Endpoints
Security and Service Accounts
OAuth and End-user accounts
Identity and Access Management
Data Protection
Appendix: Hadoop Ecosystem
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

Video Details

ISBN 139781788999519
Course Length22 hours 43 minutes
Read More

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