GCP: Complete Google Data Engineer and Cloud Architect Guide
Loại khoá học: IT Certifications
The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop
Mô tả
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 Included:
- 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)
Bạn sẽ học được gì
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
Yêu cầu
- Basic understanding of technology - superficial exposure to Hadoop is enough
Nội dung khoá học
21 sections
You, This Course and Us
02:01
Course Materials
00:11
Theory, Practice and Tests
10:26
Lab: Setting Up A GCP Account
06:59
Lab: Using The Cloud Shell
06:01
Important! Delete unused GCP projects/instances
00:20
About this section
00:15
Compute Options
09:16
Google Compute Engine (GCE)
07:38
Lab: Creating a VM Instance
05:59
More GCE
08:12
Lab: Editing a VM Instance
04:45
Lab: Creating a VM Instance Using The Command Line
04:43
Lab: Creating And Attaching A Persistent Disk
04:00
Google Container Engine - Kubernetes (GKE)
10:33
More GKE
09:54
Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container
06:55
App Engine
06:48
Contrasting App Engine, Compute Engine and Container Engine
06:02
Lab: Deploy And Run An App Engine App
07:29
Compute
21 questions
About this section
00:11
Storage Options
09:48
Quick Take
13:41
Cloud Storage
10:37
Lab: Working With Cloud Storage Buckets
05:25
Lab: Bucket And Object Permissions
03:52
Lab: Life cycle Management On Buckets
05:06
Fix for AccessDeniedException: 403 Insufficient Permission
00:30
Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage
07:09
Transfer Service
05:06
Lab: Migrating Data Using The Transfer Service
05:32
gcloud init
00:05
Lab: Cloud Storage ACLs and API access with Service Account
07:49
Lab: Cloud Storage Customer-Supplied Encryption Keys and Life-Cycle Management
09:27
Lab: Cloud Storage Versioning, Directory Sync
08:41
About this section
00:15
Cloud SQL
07:40
Lab: Creating A Cloud SQL Instance
07:54
Lab: Running Commands On Cloud SQL Instance
06:31
Lab: Bulk Loading Data Into Cloud SQL Tables
09:09
Cloud Spanner
07:25
More Cloud Spanner
09:18
Lab: Working With Cloud Spanner
06:49
Important! Delete unused GCP projects/instances
00:21
Hadoop Pre-reqs and Context
00:18
About this section
00:15
BigTable Intro
07:57
Columnar Store
08:12
Denormalised
09:02
Column Families
08:09
BigTable Performance
13:19
Getting the HBase Prompt
00:15
Lab: BigTable demo
07:39
Important! Delete unused GCP projects/instances
00:21
About this section
00:15
Datastore
14:10
Lab: Datastore demo
06:42
Datastore
3 questions
About this section
00:15
BigQuery Intro
11:03
BigQuery Advanced
09:59
Lab: Loading CSV Data Into Big Query
09:03
Lab: Running Queries On Big Query
05:26
Lab: Loading JSON Data With Nested Tables
07:28
Lab: Public Datasets In Big Query
08:16
Lab: Using Big Query Via The Command Line
07:45
Lab: Aggregations And Conditionals In Aggregations
09:51
Lab: Subqueries And Joins
05:44
Lab: Regular Expressions In Legacy SQL
05:36
Lab: Using The With Statement For SubQueries
10:45
About this section
00:04
Data Flow Intro
11:04
Apache Beam
03:42
Lab: Running A Python Data flow Program
12:56
Lab: Running A Java Data flow Program
13:42
Lab: Implementing Word Count In Dataflow Java
11:17
Lab: Executing The Word Count Dataflow
04:37
Lab: Executing MapReduce In Dataflow In Python
09:49
Lab: Executing MapReduce In Dataflow In Java
06:08
Lab: Dataflow With Big Query As Source And Side Inputs
15:50
Lab: Dataflow With Big Query As Source And Side Inputs 2
06:28
About this section
00:04
Data Proc
08:28
Lab: Creating And Managing A Dataproc Cluster
08:11
Lab: Creating A Firewall Rule To Access Dataproc
08:25
Lab: Running A PySpark Job On Dataproc
07:39
Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc
08:44
Lab: Submitting A Spark Jar To Dataproc
02:10
Lab: Working With Dataproc Using The GCloud CLI
08:19
About this section
00:04
Pub Sub
08:23
Lab: Working With Pubsub On The Command Line
05:35
Lab: Working With PubSub Using The Web Console
04:39
Lab: Setting Up A Pubsub Publisher Using The Python Library
05:52
Lab: Setting Up A Pubsub Subscriber Using The Python Library
04:08
Lab: Publishing Streaming Data Into Pubsub
08:18
Lab: Reading Streaming Data From PubSub And Writing To BigQuery
10:14
Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery
05:54
Lab: Pubsub Source BigQuery Sink
10:19
About this section
00:04
Data Lab
03:00
Lab: Creating And Working On A Datalab Instance
04:01
Lab: Importing And Exporting Data Using Datalab
12:14
Lab: Using The Charting API In Datalab
06:43
About this section
00:04
Introducing Machine Learning
08:04
Representation Learning
10:27
NN Introduced
07:35
Introducing TF
07:16
Lab: Simple Math Operations
08:46
Computation Graph
10:17
Tensors
09:02
Lab: Tensors
05:03
Linear Regression Intro
09:57
Placeholders and Variables
08:44
Lab: Placeholders
06:36
Lab: Variables
07:49
Lab: Linear Regression with Made-up Data
04:52
Image Processing
08:05
Images As Tensors
08:16
Lab: Reading and Working with Images
08:05
Lab: Image Transformations
06:37
Introducing MNIST
04:13
K-Nearest Neigbors
07:42
One-hot Notation and L1 Distance
07:31
Steps in the K-Nearest-Neighbors Implementation
09:32
Lab: K-Nearest-Neighbors
14:14
Learning Algorithm
10:58
Individual Neuron
09:52
Learning Regression
07:51
Learning XOR
10:26
XOR Trained
11:11
About this section
00:04
Lab: Access Data from Yahoo Finance
02:49
Non TensorFlow Regression
08:05
Lab: Linear Regression - Setting Up a Baseline
11:18
Gradient Descent
09:56
Lab: Linear Regression
14:42
Lab: Multiple Regression in TensorFlow
09:15
Logistic Regression Introduced
10:16
Linear Classification
05:25
Lab: Logistic Regression - Setting Up a Baseline
07:33
Logit
08:33
Softmax
11:55
Argmax
12:13
Lab: Logistic Regression
16:56
Estimators
04:10
Lab: Linear Regression using Estimators
07:49
Lab: Logistic Regression using Estimators
04:54
About this section
00:04
Lab: Taxicab Prediction - Setting up the dataset
14:38
Lab: Taxicab Prediction - Training and Running the model
11:22
Lab: The Vision, Translate, NLP and Speech API
10:53
Lab: The Vision API for Label and Landmark Detection
06:59
About this section
00:04
Live Migration
10:16
Machine Types and Billing
09:20
Sustained Use and Committed Use Discounts
07:03
Rightsizing Recommendations
02:22
RAM Disk
02:07
Images
07:45
Startup Scripts And Baked Images
07:31
About this section
00:04
VPCs And Subnets
11:14
Global VPCs, Regional Subnets
11:19
IP Addresses
11:39
Lab: Working with Static IP Addresses
05:46
Routes
07:36
Firewall Rules
15:33
Lab: Working with Firewalls
07:05
Lab: Working with Auto Mode and Custom Mode Networks
19:32
Lab: Bastion Host
07:10
Cloud VPN
07:26
Lab: Working with Cloud VPN
11:11
Cloud Router
10:31
Lab: Using Cloud Routers for Dynamic Routing
14:07
Dedicated Interconnect Direct and Carrier Peering
08:10
Shared VPCs
10:11
Lab: Shared VPCs
06:17
VPC Network Peering
10:10
Lab: VPC Peering
07:16
Cloud DNS And Legacy Networks
05:18
Networking
11 questions
About this section
00:04
Managed and Unmanaged Instance Groups
10:53
Types of Load Balancing
05:46
Overview of HTTP(S) Load Balancing
09:20
Forwarding Rules Target Proxy and Url Maps
08:31
Backend Service and Backends
09:28
Load Distribution and Firewall Rules
04:28
Lab: HTTP(S) Load Balancing
11:21
Lab: Content Based Load Balancing
07:06
SSL Proxy and TCP Proxy Load Balancing
05:05
Lab: SSL Proxy Load Balancing
07:49
Network Load Balancing
05:07
Internal Load Balancing
07:16
Autoscalers
11:51
Lab: Autoscaling with Managed Instance Groups
12:22
About this section
00:04
StackDriver
12:08
StackDriver Logging
07:39
Lab: Stackdriver Resource Monitoring
08:12
Lab: Stackdriver Error Reporting and Debugging
05:51
Cloud Deployment Manager
06:05
Lab: Using Deployment Manager
05:10
Lab: Deployment Manager and Stackdriver
08:26
Cloud Endpoints
03:48
Cloud IAM: User accounts, Service accounts, API Credentials
09:03
Cloud IAM: Roles, Identity-Aware Proxy, Best Practices
09:31
Lab: Cloud IAM
11:57
Data Protection
12:01
Operations and Security
5 questions
Introducing the Hadoop Ecosystem
01:34
Hadoop
09:42
HDFS
10:55
MapReduce
10:34
Yarn
05:29
Hive
07:19
Hive vs. RDBMS
07:10
HQL vs. SQL
07:35
OLAP in Hive
07:34
Windowing Hive
08:22
Pig
08:03
More Pig
06:38
Spark
08:54
More Spark
11:45
Streams Intro
07:44
Microbatches
05:40
Window Types
05:46
Hadoop Ecosystem
6 questions
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