GCP: Complete Google Data Engineer and Cloud Architect Guide

Discuss the Google Cloud for ML with TensorFlow & Big Data with Managed Hadoop

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by Loonycorn

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Instructor

Loonycorn

Lessons

166

Ending In:

Certification Included

access

lifetime

content

22 Hours

enrolled

2,809

Description

The Google Cloud Platform is not the most popular cloud offering out there (hello AWS!) but it may be the best cloud offering for high-end machine learning applications. That's because TensorFlow, the extremely popular deep learning technology is also from Google. This comprehensive guide to TensorFlow and the Google Cloud Platform will help put you on certification track to become a Google Data Engineer or Cloud Architect.

Access 166 lectures & 22 hours of content 24/7
Cover the material you need to pass Google Data Engineer & Cloud Architect certification exams
Explore AppEngine, Kubernetes, & Compute Engine
Discuss Big Data & Managed Hadoop w/ Dataproc, Dataflow, BigTable, BigQuery, & more
Learn what neural networks & deep learning are, how neurons work, & how neural networks are trained
Understand DevOps principles like StackDrive logging, monitoring, & cloud deployment management
Discover security, networking, & Hadoop foundations

Instructor

Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Requirements

Internet required

Terms

Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.

Course Outline

You, This Course and Us
- You, This Course and Us (2:02)
- Course Materials
Introduction
- Theory, Practice and Tests (10:28)
- Why Cloud? (9:45)
- Hadoop and Distributed Computing (9:03)
- On-premise, Colocation or Cloud? (10:07)
- Introducing the Google Cloud Platform (13:22)
- Lab: Setting Up A GCP Account (6:59)
- Lab: Using The Cloud Shell (6:01)
Compute Choices
- Compute Options (9:18)
- Google Compute Engine (GCE) (7:40)
- More GCE (8:14)
- Lab: Creating a VM Instance (5:59)
- Lab: Editing a VM Instance (4:45)
- Lab: Creating a VM Instance Using The Command Line (4:43)
- Lab: Creating And Attaching A Persistent Disk (4:00)
- Google Container Engine - Kubernetes (GKE) (10:35)
- More GKE (9:56)
- Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container (6:55)
- App Engine (6:50)
- Contrasting App Engine, Compute Engine and Container Engine (6:05)
- Lab: Deploy And Run An App Engine App (7:29)
Storage
- Storage Options (9:50)
- Quick Take (13:43)
- Cloud Storage (10:39)
- Lab: Working With Cloud Storage Buckets (5:25)
- Lab: Bucket And Object Permissions (3:52)
- Lab: Life cycle Management On Buckets (5:06)
- Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage (7:09)
- Transfer Service (5:09)
- Lab: Migrating Data Using The Transfer Service (5:32)
Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
- Cloud SQL (7:42)
- Lab: Creating A Cloud SQL Instance (7:54)
- Lab: Running Commands On Cloud SQL Instance (6:31)
- Lab: Bulk Loading Data Into Cloud SQL Tables (9:09)
- Cloud Spanner (7:27)
- More Cloud Spanner (9:20)
- Lab: Working With Cloud Spanner (6:49)
The Hadoop Ecosystem
- Introducing the Hadoop Ecosystem (1:35)
- Hadoop (9:45)
- HDFS (10:55)
- MapReduce (10:34)
- Yarn (5:29)
- Hive (7:19)
- Hive v RDBMS (7:10)
- HQL SQL (7:38)
- OLAP in Hive (7:36)
- Windowing Hive (8:22)
- Pig (8:04)
- More Pig (6:38)
- Spark (8:56)
- More Spark (11:45)
- Streams Intro (7:44)
- Microbatches (5:42)
- Window Types (5:48)
BigTable ~ HBase = Columnar Store
- BigTable Intro (7:59)
- Columnar Store (8:14)
- Denormalised (9:04)
- Column Families (8:12)
- BigTable Performance (13:21)
- Lab: BigTable demo (7:39)
Datastore ~ Document Database
- Datastore (14:12)
- Lab: Datastore demo (6:42)
BigQuery ~ Hive ~ OLAP
- BigQuery Intro (11:03)
- BigQuery Advanced (9:59)
- Lab: Loading CSV Data Into Big Query (9:03)
- Lab: Running Queries On Big Query (5:26)
- Lab: Loading JSON Data With Nested Tables (7:28)
- Lab: Public Datasets In Big Query (8:16)
- Lab: Using Big Query Via The Command Line (7:45)
- Lab: Aggregations And Conditionals In Aggregations (9:51)
- Lab: Subqueries And Joins (5:44)
- Lab: Regular Expressions In Legacy SQL (5:36)
- Lab: Using The With Statement For SubQueries (10:45)
Dataflow ~ Apache Beam
- Data Flow Intro (11:06)
- Apache Beam (3:42)
- 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 (4:37)
- Lab: Executing MapReduce In Dataflow In Python (9:50)
- Lab: Executing MapReduce In Dataflow In Java (6:08)
- Lab: Dataflow With Big Query As Source And Side Inputs (15:50)
- Lab: Dataflow With Big Query As Source And Side Inputs 2 (6:28)
Dataproc ~ Managed Hadoop
- Data Proc (8:30)
- Lab: Creating And Managing A Dataproc Cluster (8:11)
- Lab: Creating A Firewall Rule To Access Dataproc (8:25)
- Lab: Running A PySpark Job On Dataproc (7:39)
- Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc (8:44)
- Lab: Submitting A Spark Jar To Dataproc (2:10)
- Lab: Working With Dataproc Using The Gcloud CLI (8:19)
Pub/Sub for Streaming
- Pub Sub (8:25)
- Lab: Working With Pubsub On The Command Line (5:35)
- Lab: Working With PubSub Using The Web Console (4:39)
- Lab: Setting Up A Pubsub Publisher Using The Python Library (5:52)
- Lab: Setting Up A Pubsub Subscriber Using The Python Library (4:08)
- Lab: Publishing Streaming Data Into Pubsub (8:18)
- Lab: Reading Streaming Data From PubSub And Writing To BigQuery (10:14)
- Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery (5:54)
- Lab: Pubsub Source BigQuery Sink (10:20)
Datalab ~ Jupyter
- Data Lab (3:01)
- Lab: Creating And Working On A Datalab Instance (10:29)
- Lab: Importing And Exporting Data Using Datalab (12:14)
- Lab: Using The Charting API In Datalab (6:43)
TensorFlow and Machine Learning
- Introducing Machine Learning (8:06)
- Representation Learning (10:29)
- NN Introduced (7:37)
- Introducing TF (7:18)
- Lab: Simple Math Operations (8:46)
- Computation Graph (10:19)
- Tensors (9:04)
- Lab: Tensors (5:03)
- Linear Regression Intro (9:59)
- Placeholders and Variables (8:46)
- Lab: Placeholders (6:36)
- Lab: Variables (7:49)
- Lab: Linear Regression with Made-up Data (4:52)
- Image Processing (8:07)
- Images As Tensors (8:18)
- Lab: Reading and Working with Images (8:05)
- Lab: Image Transformations (6:37)
- Introducing MNIST (4:15)
- K-Nearest Neigbors as Unsupervised Learning (7:44)
- One-hot Notation and L1 Distance (7:31)
- Steps in the K-Nearest-Neighbors Implementation (9:34)
- Lab: K-Nearest-Neighbors (14:14)
- Learning Algorithm (11:00)
- Individual Neuron (9:54)
- Learning Regression (7:53)
- Learning XOR (10:29)
- XOR Trained (11:13)
Regression in TensorFlow
- Lab: Access Data from Yahoo Finance (2:49)
- Non TensorFlow Regression (8:07)
- Lab: Linear Regression - Setting Up a Baseline (11:18)
- Gradient Descent (9:58)
- Lab: Linear Regression (14:42)
- Lab: Multiple Regression in TensorFlow (9:15)
- Logistic Regression Introduced (10:18)
- Linear Classification (5:27)
- Lab: Logistic Regression - Setting Up a Baseline (7:33)
- Logit (8:35)
- Softmax (11:57)
- Argmax (12:15)
- Lab: Logistic Regression (16:56)
- Estimators (4:12)
- Lab: Linear Regression using Estimators (7:49)
- Lab: Logistic Regression using Estimators (4:54)
Vision, Translate, NLP and Speech: Trained ML APIs
- 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 (7:00)
Networking
- Virtual Private Clouds (7:05)
- VPC and Firewalls (9:27)
- XPC or Shared VPC (7:41)
- VPN (8:51)
- Types of Load Balancing (6:46)
- Proxy and Pass-through load balancing (9:51)
- Internal load balancing (6:03)
Ops and Security
- StackDriver (12:10)
- StackDriver Logging (7:41)
- Cloud Deployment Manager (6:07)
- Cloud Endpoints (3:49)
- Security and Service Accounts (7:46)
- OAuth and End-user accounts (8:33)
- Identity and Access Management (8:33)
- Data Protection (12:04)

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