Video description
Google Cloud Platform (GCP) is not only the most popular cloud offering currently, but it is possibly the best cloud offering for high-end machine learning applications thanks to TensorFlow, the popular deep learning technology from Google. This course will help you to learn the essential concepts that are needed to deploy TensorFlow applications on GCP.
The course starts with an introduction to cloud computing, Hadoop, and GCP and helps you in setting up the lab for exercises. You’ll understand various compute options, such as Google Compute Engine (GCE), and explore different storage options. As you advance, you’ll work with Cloud SQL and get an overview of BigTable and BigQuery by performing lab exercises, explore the data flow feature called Apache Beam, and use Data Proc for managing Hadoop. You’ll also learn how to use Pub/Sub on GCP and explore the features of a data lab. The course will then take you through machine learning and TensorFlow concepts and show you how to prepare a dataset to run a model and how to work with virtual machines and images. Finally, you will become familiar with networking and security concepts and get to grips with the basics of Hadoop.
By the end of this course, you’ll have developed the skills required to build TensorFlow and machine learning models on GCP.
What You Will Learn
- Explore various compute options, such as App Engine and Container Engine
- Discover how neural networks are trained
- Work with managed instance groups and balance loads
- Understand cloud computing security concepts
- Find out the functionality of interconnecting networks
- Get an overview of the basics of Hadoop
Audience
Whether you are looking to pass the Google Data Engineer or Cloud Architect Certification exams, want to build TensorFlow models and deploy them on the cloud, or want to use Google Cloud Platform in your organization, this course is for you. A basic understanding of Hadoop technology is all you need to get started with this course.
About The Author
Janani Ravi: Janani Ravi is a certified Google Cloud Architect and Data Engineer. She has earned her master's degree in electrical engineering from Stanford. She is currently in Loonycorn, a technical video content studio, of which she is a cofounder. Prior to co-founding Loonycorn, she worked at various leading companies, such as Google and Microsoft, for several years as a software engineer.
Table of contents
- Chapter 1 : You, This Course, and Us
- Chapter 2 : Introduction
-
Chapter 3 : 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
-
Chapter 4 : 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
- 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
- Chapter 5 : Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
- Chapter 6 : BigTable ~ HBase = Columnar Store.
- Chapter 7 : Datastore ~ Document Database
-
Chapter 8 : 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
-
Chapter 9 : Dataflow ~ Apache Beam
- Dataflow Intro
- Apache Beam
- Lab: Running a Python Dataflow Program
- Lab: Running a Java Dataflow 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 BigQuery as Source and Side Inputs
- Lab: Dataflow with Big Query as Source and Side Inputs 2
- Chapter 10 : Dataproc ~ Managed Hadoop
-
Chapter 11 : Pub/Sub for Streaming.
- Pub/Sub
- Lab: Working with Pub/Sub on the Command Line
- Lab: Working with Pub/Sub Using the Web Console
- Lab: Setting Up a Pub/Sub Publisher Using the Python Library
- Lab: Setting Up a Pub/Sub Subscriber Using the Python Library
- Lab: Publishing Streaming Data into Pub/Sub
- Lab: Reading Streaming Data from Pub/Sub and Writing to BigQuery
- Lab: Executing a Pipeline to Read Streaming Data and Write to BigQuery
- Lab: Pub/Sub Source BigQuery Sink
- Chapter 12 : Datalab ~ Jupyter
-
Chapter 13 : TensorFlow and Machine Learning
- Introducing Machine Learning
- Representation Learning
- Neural Networks (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 Neighbours as Unsupervised Learning
- One-hot Notation and L1 Distance
- Steps in the K-Nearest-Neighbours Implementation
- Lab: K-Nearest-Neighbours
- Learning Algorithm
- Individual Neuron
- Learning Regression
- Learning XOR
- XOR Trained
-
Chapter 14 : 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
- Chapter 15 : Vision, Translate, NLP and Speech: Trained ML APIs
- Chapter 16 : Virtual Machines and Images
-
Chapter 17 : VPCs and Interconnecting Networks
- 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
- This video explains the cloud router.
- Dedicated Interconnect Direct and Carrier Peering
- Shared VPCs
- Lab: Shared VPCs
- VPC Network Peering
- Lab: VPC Peering
- Cloud DNS and Legacy Networks
-
Chapter 18 : Managed Instance Groups and Load Balancing
- 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
- Lab: Autoscaling with Managed Instance Groups
-
Chapter 19 : Ops and Security
- Stack driver
- StackDriver Logging
- Lab: StackDriver Resource Monitoring
- Lab: StackDriver Error Reporting and Debugging
- Cloud Deployment Manager
- 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
- Chapter 20 : Appendix: Hadoop Ecosystem
Product information
- Title: GCP: Complete Google Data Engineer and Cloud Architect Guide
- Author(s):
- Release date: November 2020
- Publisher(s): Packt Publishing
- ISBN: 9781788999519
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