Getting started with Google Cloud Training Material - 2018

Getting Started
With Google Cloud
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{
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(’Module 1’) Introducing Google Cloud Platform Page 1 - 11
(’Module 2’) Compute & Storage Fundamentals Page 11 - 21
(’Module 3’) Data Analysis�on the Cloud Page 22 - 35
(’Module 4’) Scaling Data Analysis Page 35 - 49
(’Module 5’) Machine Learning Page 50 - 70
(’Module 6’) Data Processing Architecture Page 70 - 79
Summary | Continue learning with Google Cloud Page 79 - 85

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Big Data & Machine Learning
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Cloud OnBoard
Cloud OnBoard
Introducing
Google Cloud Platform:
Big Data and Machine Learning
Version #1.1
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What is Google Cloud Platform
Google Cloud Big Data products
Agenda

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Cloud computing is a continuation of a long-term
shift in how computing resources are managed
Now
2000
1980s
Next
First Wave
Server on-premises
You own everything.
It is yours to manage.
Second Wave
Data centers
You pay for the hardware
but rent the space.
Still yours to manage.
First Generation
Cloud Virtualized
data centers
You don’t rent hardware and
space, but still control
and configure virtual
machines. Pay for what
you provision.
Third Wave
Managed service
Completely elastic storage,
processing, and machine
learning so that you can
invest your energy in great
apps. Pay for what you use.
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What is Google Cloud Platform
Google Cloud Big Data products
Agenda

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Cloud OnBoard
Google’s mission is to organize
the world’s information and make
it universally accessible and
useful
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Cloud OnBoard
To organize the world’s
information,Google has been
building the most powerful
infrastructure on the planet

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Cloud OnBoard
Edge node locations >1000
Edge points of presence >100
Network
Network sea cable investments
In terms of hardware, Google Cloud has the largest cloud network, with
over 100 points of presence, and 100,000s of miles of fiber optic cable.
Unity (US, JP) 2010
Monet (US, BR) 2017
Tannat (BR, UY, AR) 2017
Junior (Rio, Santos) 2017
FASTER (US, JP, TW) 2016
PLCN (HK, LA) 2019
Indigo (SG, ID, AU) 2019
SJC (JP, HK, SG) 2013
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Cloud OnBoard
Future region and
number of zones
The network connects 15 regions,
with 3 more coming
Current region and
number of zones
S Carolina
N Virginia
Oregon
Iowa
Montreal
Los Angeles
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Frankfurt
Belgium
London
FinlandNetherlands 3
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São Paulo
3
Taiwan
Singapore
Mumbai
Sydney
Tokyo
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HongKong 3
2
3

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In terms of software, organizing the world’s information
has meant that Google needed to invent data processing methods
2002 2004 2006 2008 2010 2012 2014 2016
GFS
MapReduce TensorFlow
Bigtable
Dremel
Colossus
Flume
Megastore
Spanner
Millwheel
Pub/Sub
F1
http://research.google.com/pubs/papers.html
TPU
2018
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Cloud OnBoard
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Google Cloud opens up that innovation and infrastructure to you
2002 2004 2006 2008 2010 2012 2014 2016
ML Engine
Pub/Sub
Dataflow
Datastore
Dataflow
Cloud Storage
BigQuery
Bigtable
Dataproc
Cloud Storage
2018
Auto ML
Cloud Spanner

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A suite of products that can be put together for data processing
Foundation Databases
Data-handling
frameworks
Analytics and ML
Cloud
Storage
Cloud
Bigtable
BigQuery
Cloud Dataproc
Compute
Engine
Cloud SQL
Cloud
Datalab Cloud Dataflow
Cloud Pub/Sub
Cloud
Spanner
ML APIs
...
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Spotify illustrates the typical journey of companies that come to
Google Cloud: From lower costs to increased reliability to business
transformation
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Spend less
No-ops, Pay
for use, Secure
Flexible
Complete
Innovative
Powerful

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A suite of products that can be put together for data processing
Change how you computeChange where you compute
Improve scalability
and reliability
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Cloud OnBoard
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Atomic Fiction lowered their costs with per-minute
(now per-second) billing
https://www.youtube.com/watch?v=mBY-RjE15WA
Change where you compute

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FIS was able to improve reliability and scalability
on a massive data-processing challenge
6 BILLION
MARKET EVENTS
WRITTEN PER HOUR
1.7 GIGs
PER SECOND
PER HOUR
6 TBs
10 BN
WRITTEN
PER HOUR
BURSTS
1.7 GIGABYTES
PER SECOND
10 TERABYTES
PER HOUR
The Consolidated Audit Trail (CAT) is a data repository of all equities and options
orders, quotes, and events; FIS processed the CAT to organize 100 billion market events
into an “order lifecycle” in a 4-hour window using Cloud Bigtable.
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Rooms to Go transformed its business with data and machine learning
https://www.thinkwithgoogle.com/case-studies/rooms-to-go-improves-the-shopper-experience.html
BigQuery
Analyze
CRM
Customer Relationship Manager
customer demographics, past purchases
Rooms
to Go
Google Analytics
Premium
landing pages,
views
Combine
data
Collect
data
completely
designed room
packages

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In summary, Google Cloud offers you ways to…
Become a truly
data-driven
company
Apply machine
learning broadly
and easily
Spend less
on ops and
administration
Incorporate real-
time data into
apps and
architectures
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Module Review

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Google Cloud Platform is:
(select all of the correct options)
Cloud OnBoard
Module review
Operated by Google on the same
infrastructure it uses
A set of modular services from
which you can compose cloud-based
applications
Most cost-effective if you pre-
purchase instances on a yearly
basis
A platform on which to host
scalable and fast distributed
applications
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Google Cloud Platform is:
Cloud OnBoard
Module review
Operated by Google on the same
infrastructure it uses
A set of modular services from
which you can compose cloud-based
applications
Most cost-effective if you pre-
purchase instances on a yearly
basis
A platform on which to host
scalable and fast distributed
applications

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Resources
Google Cloud Platform https://cloud.google.com/
Datacenters https://www.google.com/about/datacenters/
Google IT security https://cloud.google.com/files/Google-
CommonSecurity-WhitePaper-v1.4.pdf
Why Google Cloud
Platform?
https://cloud.google.com/why-google/
Pricing Philosophy https://cloud.google.com/pricing/philosophy/
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Cloud OnBoard
Cloud OnBoard
Compute & Storage
Fundamentals
Version #1.1

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CPUs on demand + Demo
A global filesystem + Demo
Agenda
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Cloud OnBoard
Google Cloud provides an earth-scale computer
Data storage
Compute power
Networking

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Cloud OnBoard
Custom/changeable machine types, preemptible machines,
and automatic discounts lead to simplicity and agility
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Demo:
Create a Compute Engine instance

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In this demo, we will :
1. Create a Compute Engine instance
2. SSH into the instance
3. Install the software package git
(for source code version control)
Cloud OnBoard
Demo : Create a Compute Engine Instance
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CPUs on demand + Demo
A global filesystem + Demo
Agenda

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Cloud OnBoard
Use Cloud Storage for persistent storage and as staging
ground for import to other Google Cloud products
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Compute
Engine + Disk
Cloud SQL
BigQuery
Dataproc
Cloud Storage
Store/StageTransform
Raw data (any format)
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Ingest/ Extract
Load
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Cloud OnBoard
Create a bucket and copy the data over using the Cloud
SDK; blobs are referenced through a gs://.../ URL
sales*.csv gs://acme-sales/data/
Copy
Google Cloud Platform Project
Bucket
Objects
Data and
metadata
Bucket
Objects
Data and
metadata
gsutil cp

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Cloud OnBoard
Cloud Storage gives you durability,
reliability, and global reach
Cloud
Storage
Cloud
SQL
Compute
Engine
Transfer Services
are useful for ingest
Ingest
Store
Import
Control access at project,
bucket and/or object level
Publish
Use Cloud Storage
as staging area
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Cloud OnBoard
Control latency and availability
with zones and regions
Choose the closest
zone/region so as
to to reduce latency.
Region: North America
Zone: us-central1-a
...
Region: Europe
Zone: europe-west1-b
...
Region: ...
Zone: ...
...
Distribute your apps and data across
regions for global availability.
Distribute your apps
and data across zones
to reduce service
disruptions.

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Demo:
Interact with
Cloud Storage
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In this demo, we carry out the steps of an ingest-
transform-and-publish data pipeline manually
1. Ingest data into a Compute Engine instance
2. Transform data on the Compute Engine instance
3. Store the transformed data on Cloud Storage
4. Publish Cloud Storage data to the web
Cloud OnBoard
Demo : Interact with Cloud Storage

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Cloud OnBoard
Ingest-Transform-Publish
using core infrastructure
Cloud
Storage
Cloud
SQL
Compute
Engine
Step 1
Ingest/
Extract
Store
Import
Publish
Step 2 Step 3
Step 4
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Cloud OnBoard
Cloud Shell gives you an easy command-line
Cloud Shell comes pre-installed with the tools, libraries,
and so on you need to interact with Google Cloud Platform
Click
Do Now

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Module Review
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Compute nodes on GCP are:
(select the correct option)
❏ Allocated on demand, and you pay for the time that they are up.
❏ Expensive to create and teardown
❏ Pre-installed with all the software packages you might ever need.
❏ One of ~50 choices in terms of CPU and memory
Cloud OnBoard
Module review (1 of 2)

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Compute nodes on GCP are:
(select the correct option)
➔ Allocated on demand, and you pay for the time that they are up.
❏ Expensive to create and teardown
❏ Pre-installed with all the software packages you might ever need.
❏ One of ~50 choices in terms of CPU and memory
Cloud OnBoard
Module review answers (1 of 2)
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Google Cloud Storage is a good option for storing data that:
❏ Is ingested in real-time from sensors and other devices
❏ Will be frequently read/written from a compute node
❏ May be required to be read at some later time
❏ May be imported into a cluster for analysis
Cloud OnBoard

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Google Cloud Storage is a good option for storing data that:
❏ Is ingested in real-time from sensors and other devices
❏ Will be frequently read/written from a compute node
➔ May be required to be read at some later time
➔ May be imported into a cluster for analysis
Cloud OnBoard
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Resources
Google Cloud Platform https://cloud.google.com/compute/
Datacenters https://cloud.google.com/storage/
Pricing https://cloud.google.com/pricing/
Cloud Launcher https://cloud.google.com/launcher/
Pricing Philosophy https://cloud.google.com/pricing/philosophy/

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Cloud OnBoard
Cloud OnBoard
Data Analysis
on the Cloud
Version #1.1
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Stepping stones to transformation
Your SQL database in the cloud + Demo
Managed Hadoop in the cloud + Demo
Agenda

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Cloud OnBoard
Google Cloud Platform began in 2008, with App Engine,
a serverless way to run web applications
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Develop
2
Upload
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Autoscales Reliable
App Engine
Your code
http://googleappengine.blogspot.com/2008/04/introducing-google-app-engine-our-new.html
http://googleappengine.blogspot.com/2013/05/the-google-app-engine-blog-is-moving.html
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Cloud OnBoard
Compute
Engine
Container
Engine
App Engine
Flex
App Engine
There [was] something fundamentally
wrong with what we were doing in 2008
… We didn't get the right stepping
stones into the cloud …
-- Eric Schmidt, Executive Chairman, Google

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GCP now consists of a suite of products that together provide these
stepping stones in a business’ transformative journey
Change how you computeChange where you compute
Flexibility, scalability
and reliability
Cost effective virtual machines,
storage, Hadoop, and MySQL to
migrate your current workloads to
the public cloud.
Reliable, autoscaling messaging,
data processing, and storage.
Fully managed products for data
warehousing, data analysis,
streaming, and machine learning.
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Machine learning. This is the next
transformation … the programming
paradigm is changing. Instead of
programming a computer, you teach a
computer to learn something and it
does what you want.
Cloud OnBoard
Eric Schmidt,
Executive Chairman,
Google

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Cloud OnBoard
“If you want to teach a neural network to
recognize a cat, for instance, you don’t
tell it to look for whiskers, ears, fur,
and eyes. You simply show it thousands
and thousands of photos of cats, and
eventually it works things out.”
WIRED’s headline
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Search
People who bought ...
Spam filtering
Suggest next video
Route planning
Smart Reply
Cloud OnBoard
Machine Learning is not new,
but it is now mainstream
What’s common to all of
these use cases of Machine
Learning?
?

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There are three components in a recommendation system
RecommendingRating Training
Users rate a few houses
explicitly or implicitly
A machine learning model is
created to predict a user’s
rating of a house
For each user, the model is
applied to every unrated
house and the top 5 houses
for that user are saved.
What else is needed??
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The ML algorithm essentially clusters users and items
How often do you need to compute
the predicted ratings?
Where would you save them?
2
3
Who is like this user? Is this a good house?
Predict rating
Is this house similar to houses that
people similar to this user like?
Predicted rating = user-preference *
item-quality
1
?

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In addition to the ML algorithm, you also need
sophisticated data management
Data Collection
Data Analysis
Machine Learning
Serving
Scalable front end to collect customer actions
Data that is accessible and not silo-ed
(Re-)training and experimentation
Scalable, real-time system to serve
recommendations
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Agenda

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Choose your storage solution based on your access pattern
Capacity
Access
metaphor
Read
Write
Update
granularity
Usage
Petabytes +
Like files in a
file system
Have to copy to
local disk
One file
An object
(a “file”)
Store blobs
Gigabytes
Relational
database
SELECT rows
INSERT row
Field
No-ops SQL
database on
the cloud
Terabytes
Persistent
Hashmap
Filter objects
on property
put object
Attribute
Structured
data from
AppEngine apps
Petabytes
Key-value(s),
HBase API
scan rows
put row
Row
No-ops, high
throughput,
scalable,
flattened data
Petabytes
Relational
SELECT rows
Batch/stream
Field
Interactive SQL*
querying fully
managed warehouse
Cloud
Storage
Cloud SQL Datastore Bigtable BigQuery
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Cloud SQL is a fully managed database service
Cloud SQL
Google-managed
MySQL or Postgres
Flexible pricing
Familiar
Managed backups
Automatic replication
Fast connection from GCE & GAE
Connect from anywhere
Google Security

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Demo:
Set up rentals data
in Cloud SQL
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Cloud OnBoard
In this demo, we populate rentals data in Cloud
SQL for the recommendation engine to use:
1. Create Cloud SQL instance
2. Create database tables by importing .sql
files from Cloud Storage
3. Populate the tables by importing .csv
files from Cloud Storage
4. Allow access to Cloud SQL
5. Explore the rentals data using SQL
statements from Cloud Shell
Demo: Setup rentals data in Cloud SQL
Cloud
Storage
Cloud SQL
External
machine
Import

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Cloud OnBoard
There is a rich open-source ecosystem for big data
http://hadoop.apache.org/
http://pig.apache.org/
http://hive.apache.org/
http://spark.apache.org/

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Dataproc reduces the cost and complexity associated with
Spark and Hadoop clusters
Dataproc
Google-managed:
Hadoop
Pig
Hive
Spark
Image Versioning
Familiar
Resize in seconds
Automated cluster mgmt
Integrates with Google Cloud
Flexible VMs
Google Security
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Demo:
Recommendations ML
with Dataproc

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Cloud OnBoard
In this demo, we implement
machine learning recommendations
using Cloud Dataproc:
1. Launch Dataproc
2. Train and apply ML model
written in PySpark to create
product recommendations
3. Explore inserted rows in
Cloud SQL
Dataproc
Cloud SQL
Train
model
Show
recommendations
1
Demo: Recommendations ML with Cloud Dataproc
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Module Review

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Cloud OnBoard
Relational databases are a good choice when you need:
❏ Streaming, high-throughput writes
❏ Fast queries on terabytes of data
❏ Aggregations on unstructured data
❏ Transactional updates on relatively small datasets
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Cloud OnBoard
Relational databases are a good choice when you need:
❏ Streaming, high-throughput writes
❏ Fast queries on terabytes of data
❏ Aggregations on unstructured data
✓ Transactional updates on relatively small datasets

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Cloud OnBoard
Cloud SQL and Cloud Dataproc offer familiar tools (MySQL and
Hadoop/Pig/Hive/Spark). What is the value-add provided by Google Cloud Platform?
❏ It’s the same API, but Google implements it better
❏ Google-proprietary extensions and bug fixes to MySQL, Hadoop, and so on
❏ Fully-managed versions of the software offer no-ops
❏ Running it on Google infrastructure offers reliability and cost savings
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Cloud OnBoard
Cloud SQL and Cloud Dataproc offer familiar tools (MySQL and
Hadoop/Pig/Hive/Spark). What is the value-add provided by Google Cloud Platform?
❏ It’s the same API, but Google implements it better
❏ Google-proprietary extensions and bug fixes to MySQL, Hadoop, and so on
✓ Fully-managed versions of the software offer no-ops
✓ Running it on Google infrastructure offers reliability and cost savings

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Cloud SQL
Cloud Dataproc
Cloud Solutions
Resources
https://cloud.google.com/sql/
https://cloud.google.com/dataproc/
https://cloud.google.com/solutions/
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Cloud OnBoard
Cloud OnBoard
Scaling Data Analysis
Version #1.1

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Fast random access
Warehouse and interactively query petabytes
Interactive, iterative development + Demo
Agenda
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Cloud OnBoard
Choosing where to store data on GCP
unstructured
Data analytics
workload
Transactional
workload
structuredNeed
MOBILE
SDKs
SQL
Horizontal
scalability
No-SQL Millisecond
Latency
Latency in
secondsCloud
Spanner
Cloud
SQL
Firebase
Storage
Cloud
Storage
Firebase
Realtime DB
Cloud
Datastore
Cloud
Bigtable
BigQuery
Need
MOBILE
SDKs
One
database
enough

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Cloud OnBoard
Use cloud spanner if you need globally consistent data or more
than one Cloud SQL instance
Source:
https://quizlet.com/blog/
quizlet-cloud-spanner
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Cloud OnBoard
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Cloud
Datastore
Bigtable
Cloud
Storage
Cloud SQL
Cloud
Spanner
BigQuery
Type NoSQL
document
NoSQL
wide column
Blobstore Relational
SQL for OLTP
Relational
SQL for OLTP
Relational
SQL for OLAP
Transactions Yes Single-row No Yes Yes No
Complex
queries
No No No Yes Yes Yes
Capacity Terabytes+ Petabytes+ Petabytes+ 500 GB Petabytes Petabytes+
Unit size 1 MB/entity ~10 MB/cell
~100 MB/row
5 TB/object Determined
by DB engine
10,240 MiB/
row
10 MB/row
Comparing storage options: technical details

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Cloud
Datastore
Bigtable
Cloud
Storage
Cloud SQL
Cloud
Spanner
BigQuery
Type NoSQL
document
NoSQL
wide column
SQL for OLTP
Relational
SQL for OLTP
Relational
SQL for OLAP
Best for Getting
started, App
Engine
applications
“Flat” data,
Heavy
read/write,
events,
analytical
data
Structured
and
unstructured
binary or
object data
Web
frameworks,
existing
applications
Large-scale
database
applications
(> ~2 TB)
Interactive
querying,
offline
analytics
Use cases Getting
started, App
Engine
applications
AdTech,
Financial
and IoT data
Images,
large media
files,
backups
User
credentials,
customer
orders
Whenever
high I/O,
global
consistency
is needed
Data
warehousing
Comparing storage options: use cases
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Cloud OnBoard
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Bigtable is meant for high throughput data where access is primarily
for a range of Row Key prefixes
NASDAQ#1426535612045
...
MD:SYMBOL:
ZXZZT
...
Row Key Column data
MD:LASTSALE:
600.58
...
MD:LASTSIZE:
300
...
MD:TRADETIME:
1426535612045
...
MD:EXCHANGE:
NASDAQ
...
Tables should be tall and narrow
Store changes as new rows
Bigtable will automatically
compact the table

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Cloud OnBoard
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Short meaningful column names reduce storage and RPC overhead
NASDAQ#1426535612045
MD:SYMBOL:
ZXZZT
Row Key Column data
MD:LASTSALE:
600.58
MD:LASTSIZE:
300
MD:TRADETIME:
1426535612045
MD:EXCHANGE:
NASDAQ
Design row key with most
common query in mind
Design row key to minimize hotspots
Column families is a quick
way to get some hierarchy
Use short column names
Designed for sparse tables
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Cloud OnBoard
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Can work with Bigtable using the HBase API
import org.apache.hadoop.hbase.*;
import org.apache.hadoop.hbase.client.*;
import org.apache.hadoop.hbase.util.*;
byte[] CF = Bytes.toBytes("MD"); // column family
Connection connection = ConnectionFactory.createConnection(...)
Table table = null;
try {
table = connection.getTable(TABLE_NAME);
Put p = new Put(Bytes.toBytes("NASDAQ#GOOG #1234561234561"));
p.addColumn(CF, Bytes.toBytes("SYMBOL"), Bytes.toBytes("GOOG"));
p.addColumn(CF, Bytes.toBytes("LASTSALE"), Bytes.toBytes(742.03d));
...
table.put(p);
} finally {
if (table != null) table.close();
}

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Cloud
Datastore
Bigtable
Cloud
Storage
Cloud SQL
Cloud
Spanner
BigQuery
Type NoSQL
document
NoSQL
wide column
SQL for OLTP
Relational
SQL for OLTP
Relational
SQL for OLAP
Complex
queries
~100 MB/row
by DB engine
10,240 MiB/
row
10 MB/row
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Cloud OnBoard
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Cloud
Datastore
Bigtable
Cloud
Storage
Cloud SQL
Cloud
Spanner
BigQuery
Type NoSQL
document
NoSQL
wide column
SQL for OLTP
Relational
SQL for OLTP
Relational
SQL for OLAP
Best for Getting
started, App
Engine
applications
“Flat” data,
Heavy
read/write,
events,
analytical
data
Structured
and
unstructured
binary or
object data
Web
frameworks,
existing
applications
Large-scale
database
applications
(> ~2 TB)
Interactive
querying,
offline
analytics
Use cases Getting
started, App
Engine
applications
AdTech,
Financial
and IoT data
Images,
large media
files,
backups
User
credentials,
customer
orders
Whenever
high I/O,
global
consistency
is needed
Data
warehousing

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Fast random access
Agenda
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Cloud OnBoard
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BigQuery is a fully managed data warehouse that lets you do ad-hoc
SQL queries on massive volumes of data
Project X
Dataset A Dataset B
Project Y
Dataset C Dataset D
Table 1
Table 2
Table 1
Table 2
Table 1
Table 2
Table 1
Table 2
BigQuery Service

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Cloud OnBoard
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A demo of BigQuery on a 10 billion-row dataset shows what it is
and what it can do
#standardsql
SELECT
language, SUM(views) as views
FROM `bigquery-samples.wikipedia_benchmark.Wiki10B`
WHERE
title like "%google%"
GROUP by language
ORDER by views DESC
Familiar, SQL 2011 query
language
Interactive ad-hoc analysis
of petabyte-scale databases
No need to provision
clusters
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Cloud OnBoard
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Three ways of loading data into BigQuery
POST
Serverless
ETL
CSV
JSON
AVRO
Google
Sheets
Files on disk or Cloud
Storage
Stream Data Federated data source

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Cloud OnBoard
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With Federated data sources, you can directly query files on
Cloud Storage, without having to ingest them into BigQuery
Also: Google Drive, Bigtable
Also: JSON/Avro/Google Sheet
Can also pass in a schema
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Cloud OnBoard
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Cloud
Datastore
Bigtable
Cloud
Storage
Cloud SQL
Cloud
Spanner
BigQuery
Type NoSQL
document
NoSQL
wide column
SQL for OLTP
Relational
SQL for OLTP
Relational
SQL for OLAP
Complex
queries
~100 MB/row
by DB engine
10,240 MiB/
row
10 MB/row

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Cloud OnBoard
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12
13
14
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Cloud
Datastore
Bigtable
Cloud
Storage
Cloud SQL
Cloud
Spanner
BigQuery
Type NoSQL
document
NoSQL
wide column
SQL for OLTP
Relational
SQL for OLTP
Relational
SQL for OLAP
Best for Getting
started, App
Engine
applications
“Flat” data,
Heavy
read/write,
events,
analytical
data
Structured
and
unstructured
binary or
object data
Web
frameworks,
existing
applications
Large-scale
database
applications
(> ~2 TB)
Interactive
querying,
offline
analytics
Use cases Getting
started, App
Engine
applications
AdTech,
Financial
and IoT data
Images,
large media
files,
backups
User
credentials,
customer
orders
Whenever
high I/O,
global
consistency
is needed
Data
warehousing
1
2
3
5
6
7
8
9
10
11
12
13
14
15
16
17
Fast random access
Agenda

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Cloud OnBoard
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Increasingly, data analysis and machine learning are carried
out in self-descriptive, shareable, executable notebooks
Code
Output
Markup
Share
A typical notebook
contains code,
charts, and
explanations
Image Source:
Git Logo from
Wikipedia
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Cloud OnBoard
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Datalab is an open-source notebook built on Jupyter (IPython)
Analyze data in BigQuery,
Compute Engine or Cloud Storage
Use existing
Python packages
Datalab is free—just pay
for Google Cloud resources

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Datalab notebooks are developed in an iterative, collaborative process
Development
Process in
Cloud Datalab
PHASE 1
Write code in
Python
PHASE 5
Share and
collaborate
PHASE 2
Run cell
(Shift+Enter)
PHASE 4
Write
commentary in
markdown
PHASE 3
Examine Output
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Datalab supports BigQuery
%%sql
To
Pandas

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Create ML dataset
with BigQuery
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Cloud OnBoard
In this demo, we use BigQuery to create a
dataset that we later use to build a taxi
demand forecast system using Machine Learning.
● What kinds of things affect taxi demand?
● What are some ways to measure “demand”?
Demo: Create ML dataset with
BigQuery

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Demo: Create ML dataset with BigQuery
In this demo, we use BigQuery to create a dataset that we later
use to build a taxi demand forecast system using Machine Learning.
1. Use BigQuery and Datalab to explore and visualize data
2. Build a Pandas dataframe that will be used as the training
dataset for machine learning using TensorFlow
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Module Review

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Module review
Match the use case on the left with the product on the right
Global consistency needed
High-throughput writes of wide-column data
Warehousing structured data
Develop Big Data algorithms interactively in Python
1. Datalab
2. BigTable
3. BigQuery
4. Spanner
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Module review
Global consistency needed (4)
High-throughput writes of wide-column data (2)
Warehousing structured data (3)
Develop Big Data algorithms interactively in Python (1)
1. Datalab
2. BigTable
3. BigQuery
4. Spanner

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Cloud OnBoard
Machine Learning
Version #1.1
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Machine learning with TensorFlow + Demo
Pre-built machine learning models + Demo
Agenda

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TensorFlow is an open source library that underlies many Google products
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Demo: Playing with neural networks to learn what they are
http://playground.tensorflow.org/

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Supervised machine learning requires features and labels
…
…
Neural Network
…
Input
features Prediction
Cost
Neural network image by Dake, Mysid [CC BY 1.0], via Wikimedia Commons
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Machine Learning with TensorFlow involves four steps:
1
Gather
Data
Gather training data (input features and labels)
Create model
Train the model based on input data
Use the model on new data
2
Create
3
Train
4
Use

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Gather training data and select input features
Input features
targetdiscard
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Gather
Data
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All input features need to be numeric
1
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Data
One-hot encodingUse as-is

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Create a neural network model, defining the number of feature columns
and hidden units
2
Create
npredictors
nhidden
noutputs
…
…
estimator = DNNRegressor(hidden_units=[5], feature_columns=[...])
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Train the model on the collected data
3
Train
estimator.fit(predictors, targets, steps=1000)
…
…
Predicted
value of
taxicab
demand
model
True value of
taxicab
demand
CostUpdate
model based
on Cost
npredictors

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Use
True value of
taxicab demand
rain
Max temp
…
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Predicted value
of taxicab
demand
Update model
based on
Cost
model
Cost
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input = pd.DataFrame.from_dict(data =
{'dayofweek' : [4, 5, 6],
'mintemp' : [60, 15, 60],
'maxtemp' : [80, 80, 65],
'rain' : [0, 0.8, 0]})
# read trained model from /tmp/trained_model
estimator = DNNRegressor(model_dir='/tmp/trained_model',
hidden_units=[5])
pred = estimator.predict(input.values)
print pred
4
Use

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Demo 2 Part 2:
Carry out ML
with TensorFlow
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Demo 2, Part 2: Carry out ML with TensorFlow
Neural Network
Inputs Prediction
In this demo, we build a neural network to predict taxicab demand
on a day-by-day basis using TensorFlow.

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Machine learning with TensorFlow + Demo
Pre-built machine learning models + Demo
Agenda
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The accuracy of a ML problem is driven largely by the size and quality
of the dataset; this is why ML requires massive compute
Size of dataset
Scale of Compute Problem
Accuracy
https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html

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CloudML Engine simplifies the use of Distributed TensorFlow
...
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dataset
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ML APIs are pre-trained ML models (trained off Google’s data) for common
tasks; they are accessible through REST APIs
Use your own data to train models Machine Learning as an API
Cloud
Vision API
Cloud
Translation API
Cloud
Natural Language
API
Cloud
Speech API
Cloud Machine
Learning Engine
TensorFlow
Cloud Video
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Logo Detection
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Face detection
"detectionConfidence" : 0.93568963,
"joyLikelihood" : "VERY_LIKELY",
"panAngle" : 4.150538,
"sorrowLikelihood" : "VERY_UNLIKELY",
"tiltAngle" : -19.377356,
"underExposedLikelihood" : "VERY_UNLIKELY",
"blurredLikelihood" : "VERY_UNLIKELY"
"faceAnnotations" : [
{
"headwearLikelihood" : "VERY_UNLIKELY",
"surpriseLikelihood" : "VERY_UNLIKELY",
rollAngle" : -4.6490049,
"angerLikelihood" : "VERY_UNLIKELY",
"landmarks" : [
{
"type" : "LEFT_EYE",
"position" : {
"x" : 691.97974,
"y" : 373.11096,
"z" : 0.000037421443
}
},
...
],
"boundingPoly" : {
"vertices" : [
{
"x" : 743,
"y" : 449
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...

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Web annotations
{
"entityId": "/m/016ms7",
"score": 1.44038,
"description": "Ford Anglia"
}
{
"entityId": "/m/0gff2yr",
"score": 5.92256,
"description": "ArtScience Museum"
}
{
"entityId": "/m/0h898pd",
"score": 7.4162,
"description": "Harry Potter (Literary Series)"
}
CC-BY 2.0 Rev Stan: https://www.flickr.com/photos/revstan/6865880240
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Try it in the browser with your own images
cloud.google.com/vision

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The Translation API supports 100+ languages
https://cloud.google.com/translate/
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Wootric uses the Cloud Natural Language API (entity and sentiment) to
make sense of qualitative customer feedback

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Extracted entities are tied into a knowledge graph
Joanne "Jo" Rowling, pen names J. K. Rowling and Robert Galbraith,
is a British novelist, screenwriter and film producer best known as
the author of the Harry Potter fantasy series
{
"name": "Joanne 'Jo' Rowling",
"type": "PERSON",
"metadata": {
"mid": "/m/042xh",
"wikipedia_url": "http://en.wikipedia.org/wiki/J._K._Rowling"
}
{
"name": "British",
"type": "LOCATION",
"metadata": {
"mid": "/m/07ssc",
"wikipedia_url": "http://en.wikipedia.org/wiki/United_Kingdom"
}
{
"name": "Harry Potter",
"type": "PERSON",
"metadata": {
"mid": "/m/078ffw",
"wikipedia_url":
"http://en.wikipedia.org/wiki/Harry_Potter"
}
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When you analyze sentiment, you get a score (positive/negative) as well
as a magnitude (how intense?)
{
"documentSentiment": {
"score": 0.8,
"magnitude": 0.8
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}
The food was excellent, I would definitely go back!

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The Cloud Speech API can be used to transcribe audio to text
http://cloud.google.com/speech
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Like the Vision API, the Video Intelligence API can identify labels in a
video, along with a timestamp
{
"description": "Bird's-eye view",
"language_code": "en-us",
"locations": {
"segment": {
"start_time_offset": 71905212,
"end_time_offset": 73740392
},
"confidence": 0.96653205
}
}
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Demo 2 Part 3:
Machine Learning APIs
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Use several of the Machine Learning
APIs (Vision, Translate, Natural
Language Processing, Speech) from
Python
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Demo 2, Part 3: Machine
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“How much is this car worth?”
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“Hi Ocado,
I love your website. I have children so it’s
easier for me to do the shopping online.
Many thanks for saving my time!
Regards”
Feedback Customer is happy
Improves natural
language processing
of customer service
claims
“Thanks to the Google Cloud Platform, Ocado was able to use
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50%
of enterprises will be
spending more per annum
on bots and chatbot
creation than traditional
mobile app development by
2021 – Gartner
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Use
Dialogflow to
create a new
shopping
experience
Custom image
model to
price cars
Build off NLP
API to route
customer
emails
Use Vision
API as-is to
find text in
memes

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Confidential & Proprietary
UPDATEDEPLOYEVALUATETUNE ML MODEL
PARAMETERS
ML MODEL DESIGN
DATA
PREPROCESSING
Introducing Cloud AutoML
A technology that can automatically create a Machine Learning Model
UPDATEDEPLOYEVALUATETUNE ML MODEL
PARAMETERS
ML MODEL
DESIGN
DATA
PREPROCESSING
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Handbag Shoe Hat
Cloud AutoML
Cloud AutoML Vision
Model is now trained and ready to make prediction.
This model can scale as needed to adapt to customer demands.
Upload and label
images
Train your model
in minutes or one day Evaluate

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Demo:
Module Review
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Match the use case on the left with the
product on the right
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Module review
Create, test new machine learning methods
No-ops, custom machine learning applications at scale
Automatically reject inappropriate image content
Build application to monitor Spanish twitter feed
Transcribe customer support calls
1. Vision API
2. TensorFlow
3. Speech API
4. Cloud ML
5. Translation API

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Module review
Create, test new machine learning methods (2)
No-ops, custom machine learning applications at scale (4)
Automatically reject inappropriate image content (1)
Build application to monitor Spanish twitter feed (5)
Transcribe customer support calls (3)
1. Vision API
2. TensorFlow
3. Speech API
4. Cloud ML
5. Translation API
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Resources (1 of 2)
Cloud Spanner https://cloud.google.com/spanner/
Cloud Bigtable https://cloud.google.com/bigtable/
Google BigQuery https://cloud.google.com/bigquery/
Cloud Datalab https://cloud.google.com/datalab/
TensorFlow https://www.tensorflow.org/

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Resources (2 of 2)
Cloud Machine Learning https://cloud.google.com/ml/
Vision API https://cloud.google.com/vision/
Translation API https://cloud.google.com/translate/
Speech API https://cloud.google.com/speech/
Video Intelligence API
https://cloud.google.com/video-
intelligence
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Data Processing Architecture
Cloud OnBoard
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Message-oriented architectures
Serverless data pipelines
GCP Reference Architecture
Agenda
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Asynchronous processing is useful for
long-lived tasks or to have loose
coupling between two systems
P1 P2 P3 Producers
C1 C2 C3 Consumers
Potential use cases:
1. Send an SMS
2. Train ML model
3. Process data from multiple sources
4. Weekly reports …
Message
Queue

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For robust asynchronous processing, you need:
P1 P2 P3
C1 C2 C3
1. A global, highly available queue
2. Scale without over-provisioning
4. Reliable delivery of messages
3. Queue
must be
interoperable
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Pub/Sub provides a no-ops, serverless global message queue

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Dataflow offers NoOps data pipelines in Java and Python
p = beam.Pipeline(options=options)
p.run();
traffic = lines | beam.Map(parse_data).with_output_types(unicode)
| beam.GroupByKey() # (sensor, [speed])
output = traffic | beam.io.WriteToText(‘gs://...]’)
lines = p | beam.io.ReadFromText(‘gs://…’)
Each of these steps is run
in parallel and autoscaled
by execution framework
Open-source API (Apache
Beam) can be executed on
Flink, Spark, etc. also
Group
Transform 3
Transform 4
Write
Read
Transform 1
Input
Output
Transform 2 | beam.Map(get_speedbysensor) # (sensor, speed)
| beam.Map(avg_speed) # (sensor, avgspeed)
| beam.Map(lambda tup: '%s: %d' % tup))
Map
Group-By
Reduce

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Same code does real-time and batch
options = PipelineOptions(pipeline_args)
options.view_as(StandardOptions).streaming = True
p = beam.Pipeline(options=options)
lines = p | beam.io.ReadStringsFromPubSub(input_topic)
traffic = (lines
|
beam.Map(parse_data).with_output_types(unicode)
| beam.Map(get_speedbysensor) # (sensor,
speed)
| beam.WindowInto(window.FixedWindows(15, 0))
| beam.GroupByKey() # (sensor, [speed])
| beam.Map(avg_speed) # (sensor, avgspeed)
| beam.Map(lambda tup: '%s: %d' % tup))
traffic | beam.io.WriteStringsToPubSub(output_topic)
p.run()
Cloud
Pub/Sub
Cloud
Dataflow
BigQuery
Cloud
Storage
Cloud
Pub/Sub
Cloud
Storage
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Dataflow does ingest, transform, and load; consider using it
instead of Spark

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Choosing where to store data on GCP
unstructured
Data analytics
workload
Transactional
workload
structured
SQL
Horizontal
scalability
No-SQL
Millisecond
Latency
Latency in
seconds
Cloud
Spanner
Cloud
SQL
Cloud
Storage
Cloud
Datastore
Cloud
Bigtable
BigQuery
One
database
enough

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Run Spark/Hadoop jobs on Cloud Dataproc
Cloud
Dataproc
Input and Output
Data Sources
Cloud
Storage
Cloud
Bigtable
BigQuery
Client
Direct
access
API
Applications on
cluster
Input and
output
connectors
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On GCP, you can have the same data processing pipeline for
processing both batch and stream
Cloud
Storage
Raw logs,
files, assets,
Google
Analytics data,
and so on
Events,
metrics,
and so on Stream
Batch
Bigtable
B CA
Cloud ML
Engine
Applications
and Reports
Cloud
Datalab
Data Studio
Dashboards/BI
Co-workers
Cloud
Pub/Sub
Cloud
Dataflow
BigQuery

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Demo:
Module Review
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Cloud OnBoard
Module review
A. Decoupling producers and consumers of data
in large organizations and complex systems
B. Scalable, fault-tolerant multi-step
processing of data
1. Cloud Dataflow
2. Cloud Pub/Sub

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Cloud OnBoard
Module review
A. Decoupling producers and consumers of data
in large organizations and complex systems
B. Scalable, fault-tolerant multi-step
processing of data
1. Cloud Dataflow
2. Cloud Pub/Sub
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Resources (1 of 2)
Cloud Pub/Sub https://cloud.google.com/pubsub/
Cloud Dataflow https://cloud.google.com/dataflow/
Processing media using
Cloud Pub/Sub and
Compute Engine
https://cloud.google.com/solutions/me
dia-processing-pub-sub-compute-engine

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Resources (2 of 2)
Reverse Geocoding of
Geolocation Telemetry
in the Cloud Using the
Maps API
https://cloud.google.com/solutions/reverse-
geocoding-geolocation-telemetry-cloud-maps-
ap
Using Cloud Pub/Sub for
Long-running Tasks
https://cloud.google.com/solutions/us
ing-cloud-pub-sub-long-running-tasks
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Cloud OnBoard
Summary
Version #1.1

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Cloud OnBoard
An Evolving Cloud
Your kit, someone
else’s building.
Yours to manage.
1st Wave
Standard virtual
kit,for rent.
Still yours to manage.
2nd Wave
Invest your energy
in great apps
3rd Wave
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Cloud OnBoard
Google Cloud provides a way to take advantage of Google’s
investments in infrastructure and data processing innovation
2002
Cloud
Storage
2004 2006 2008 2010 2012 2014 2016
DataProc Bigtable BigQuery
Cloud
Storage
DataFlow
DataStore
DataFlow
Pub/Sub
ML Engine
2018
Auto ML
Cloud
Spanner
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Cloud OnBoard
Typical Big Data Processing
Programming
Resource
provisioning
Performance
tuning
Monitoring
Reliability
Deployment &
configuration
Handling
growing scale
Utilization
improvements
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Cloud OnBoard
Big Data with Google: Focus on insight, not infrastructure.
Programming

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Cloud OnBoard
In summary, GCP offers you ways to...
To get the most
out of data and
secure competitive
advantage.
Create citizen
data scientists
Transform your
organization into
a truly data driven
company. Putting
tools into hands of
domain experts.
Apply machine
learning broadly
and easily
We make it simple and
practical to
incorporate machine
learning models
within custom
applications.
We’ve “automated
out” the complexity
of building and
maintaining data
and analytics
systems.
Spend less on ops
and administration
Incorporate real-time
data into apps and
architectures
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Cloud OnBoard
Today
Google Cloud Platform
Fundamentals: Big Data
and Machine Learning
Next Steps on your Google Cloud learning journey
1 2 3
Tomorrow
Complete hands-on labs:
Baseline: Data, ML, AI quest
google.qwiklabs.com
Future
Find more training online
cloud.google.com/training
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Cloud OnBoard
Complete 10 hands-on labs free on Qwiklabs
by 30 April, and receive $200 in GCP credits
[Only for Cloud OnBoard Attendees]
1
2
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Receive a follow up email after event
Username
Password
Create Qwiklabs account with the email
you used to register for Cloud OnBoard
Open your email and confirm account
4 Return to Qwiklabs and log in
5 Enroll in the Baseline: Data, ML, AI quest and
take your first lab!
6
Complete all 10 labs and we will send you an
email after 30 April with instructions to redeem
the $200 credits. Make sure you opt-in to receive
emails from Qwiklabs.
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Cloud OnBoard
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Go to
https://www.coursera.org/voucher/CloudOnBoardML
Activate voucher and sign
up for a free account
Enroll in Serverless Data
Analysis with Google BigQuery
and Cloud Dataflow for Free
-Limited period offer!
Explore other Courses at
Coursera.org/Googlecloud
To help you get started
Activate your voucher now for a free course worth $99!
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Cloud OnBoard
Make Google Cloud certification your goal!
cloud.google.com/certification
Find study guides, tips, practice
exams, and testing sites
Confidential & Proprietary
A special offer for Cloud Onboard Singapore attendees:
Visit https://goo.gl/bmJwwk before May 18th to enroll, and eligible
startups* receive $3,000 in Google Cloud and Firebase credits.
Google Cloud Startup Program
$3,000
g.co/cloudstartups
cloud.startups@google.com
Google Cloud is a perfect fit for launching
and scaling your early-stage startup. What’s an eligible
startup?
• Raised no more than a Series A
• Less than 5 years old
• Are located in our approved
countries
• Have not participated in
the Google Cloud Startup
program before
in credits
84

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Be part of the
GCP User Group SG Community!
or bit.ly/gcpusergroupsg
Learn from leads, users,
and tech expertsLearn
Network, share, learn -
all about Google CloudConnect
Gain access to the
Google Cloud team
and the latest
capabilities
Access
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Cloud OnBoard
Resources
Big data and machine learning blog https://cloud.google.com/blog/big-data/
Google Cloud Platform blog https://cloudplatform.googleblog.com/
Google Cloud Platform curated articles https://medium.com/google-cloud
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<MoreTraining>
https://cloud.google.com/training/
</MoreTraining>
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