SlideShare uma empresa Scribd logo
1 de 94
Baixar para ler offline
Google Data Analysis
Technologies
Andres L. Martinez a.k.a almo
Google Developer Relations Manager
@davilagrau
Contents
● BigQuery
● Data Analysis
● Machine
Learning
● Cloud ML
● Google Assistant
● Cloud ML APIs
BigQuery
BigQuery: 100% serverless data warehouse
Google
BigQuery
Fully Managed and Serverless
Google Cloud’s Enterprise Data
Warehouse for Analytics
Petabyte-Scale and Fast
Convenience of SQL
Encrypted, Durable and Highly
Available
BigQuery is a great choice because:
Near-real
time analysis
of massive
datasets
No-ops;
Pay for use
Durable
(replicated),
inexpensive
storage
Immutable
audit logs
Mashing up
different
datasets to
derive insights
10 B rows
Sample query - Processes over 10
billion rows in less than 10
seconds
SELECT
language, SUM(views) as views
FROM
wikipedia_benchmark.Wiki10B
WHERE
regexp_match(title,"G.*o.*o.*g")
GROUP by language
ORDER by views DESC
BigQuery = Massively Parallel Processing query
with the petabit network and thousands of servers
SQL QueryPetabit Network
BigQuery
Storage Compute
Streaming Ingest
Fast Batch Load
DataFlow
DataProc
Load data using bq tool, web UI, or API
Create, append or
overwrite table
CSV, JSON or
AVRO format
Data Studio
For business analysts
Beautiful reports
Drive-based collaboration experience
No technical expertise required
Connects to many sources: BigQuery, Adwords, Google
Analytics, Google Sheets, YouTube Analytics, etc.
Integrating with Google Data Studio
1 Navigate to DataStudio
to create a new
dashboard
2 Create a new Data
Source
3 Select the type of Data
Source to use
4 Authorize
Person
A
Person
B
Collaboration
Sample Dashboard
Machine Learning
Why Machine Learning?
★ Allows to solve problems we don’t have exact solution
for.
○ E.g. recommendations, predictions, clustering.
★ Given y = F(X), where we observe y, we can estimate F.
★ Becomes better with more data
○ when hard coded solution usually becomes worse with more code :)
Google Products Using Machine Learning
Big Data Challenges
★ Variety of data
★ Learning many things at once
★ Small data where matters
Why is this time different?Photo by Emily Morter on Unsplash
Photo by Sharon Pittaway on Unsplash
Innovation
Photo by Alex Holyoake on Unsplash
Fast{er}
Photo by Josh Calabrese on Unsplash
Focus on the user
Deep Learning
Input
Out
Input
Out
Deep Learning for Perception Tasks
Input
Out
Deep Learning for Perception Tasks
Input
Out
Deep Learning for Perception Tasks
Input
Out
Deep Learning for Perception Tasks
Input
Out
Deep Learning for Perception Tasks
Deep Learning combines many components
Predictions
GoogLeNet
Recurrent Neural Network
The Machine Learning Spectrum
TensorFlow Cloud Machine Learning Machine Learning APIs
BYOML skills
(Friendly Machine Learning)
Pre-packaged ML
TensorFlow Ecosystem
Researchers Developers
Data
Scientists
TensorFlow Core Execution Engine
CPU GPU Android iOS ...
C++ FrontendPython Frontend ...
TensorFlow: Google backed
★ Google supported (growing army of
engineers are working on improving it).
★ Used in 100s of products across Google
Simple example:
How can I use this?
Predictive Example
Going to deep neural network is easy:
Understanding Images
Image Classification
Scaling Out
TensorFlow scales with number of Machines.
You can use Google Cloud ML or Docker containers in VMs.
https://arxiv.org/abs/1604.00981
TensorFlow Serving: Serving models in production
Open Source project.
Check it out:
http://github.com/tenso
rflow/serving
Google Assistant
Voice Kit
Google Home — voice-activated speaker powered
The Google Assistant — A conversation between you and
Google that helps you get more done in your world.
Actions on Google — How developers can extend the
assistant (via Conversation Actions)
Direct Actions
Conversation Actions
Use machine
learning to
understand what
users are saying
“Ok Google, talk to personal chef”
Conversation API, Actions SDK
Invoke “personal chef” action
“Sure, here’s personal chef.
Hi, I’m your personal chef, what
are you in the mood for?”
Speech to Text
“What protein would you
like to use?”
“Well, it’s kind of cold outside, so I’d like
something to …”
Text to Speech
“Sure, here’s your personal chef”
Speech to Text, NLP,
Knowledge Graph,
ML Ranking, User
Profile, Text to
Speech
APIs
Confidential & ProprietaryGoogle Cloud Platform 53
So…. Why APIs?
{ Google Cloud Platform }
1. We want to offer businesses the tools to differentiate by offering a powerful set of APIs
that enable apps to see, hear and understand the world
2. Reduce your Time to Market (TMM) when launching your next-generation app
3. Provide you easy access to machine learning technology to give any developer the
freedom to work in the language and tools they want
4. Provide virtually limitless scalability to your application without needing to manage
back-end servers running deep learning
Pre-Trained Machine Learning Models
Fully trained ML models from Google Cloud that allow a general developer to
take advantage of rich machine learning capabilities with simple REST based
services.
Introducing
Cloud Natural Language API
Sentiment analysis and entity
recognition for text
Confidential & ProprietaryGoogle Cloud Platform 56
Features
Extract sentence, identify parts of
speech and create dependency parse
trees for each sentence
Identify entities and label by types such
as person, organization, location, events,
products and media
Understand the overall sentiment of a
block of text
Access via REST API. Text can be
uploaded in the request or integrated
with Google Cloud Storage
Syntax Analysis Entity Recognition
Sentiment Analysis Integrated REST API
Cloud Vision API
Insight from images with our powerful
Cloud Vision API
Confidential & ProprietaryGoogle Cloud Platform 58
Faces: Faces, facial landmarks,
emotions
OCR: Read and extract text, with
support for > 10 languages
Photo credit Getty Images
Label: Detect entities from furniture to
transportation
Logos: Identify product logos
Landmarks & Image Properties
Detect landmarks & dominant
color of image
Safe Search: Detect explicit content -
adult, violent, medical and spoof
Cloud Vision API
Call API from anywhere, with support for embeddable images, and Google Cloud Storage
Use case
Let’s Party
Party planning
● Finding people @Twitter
● Cloud Vision API
● Custom classifier
(k-means)
Google Cloud Console
We need to have access so we can
add hash tag to intro slide
Google Cloud
Console
Show Me
the code!
Main shellplus_contacts = get_plus_contacts()
print "Processing %d contacts" % len(plus_contacts)
for plus_id in plus_contacts:
plus_profile = get_plus_profile(plus_id)
image_uri = plus_profile['image']['url'].replace("?sz=50","?sz=250")
image_data = analyze_img(image_uri)
if image_data is not None:
print(image_uri)
if 'labelAnnotations' in image_data['responses'][0]:
for label in image_data['responses'][0]['labelAnnotations']:
print label['description']; label['score']; image_uri
get_plus_contacts: oAuth
storage = Storage('/home/almo/dev/keys/ex1/oAuth_credentials.dat')
credentials = storage.get()
if credentials is None or credentials.invalid:
PEOPLE_API='https://www.googleapis.com/auth/contacts.readonly'
flow = flow_from_clientsecrets('/home/almo/dev/keys/ex1/oAuth_key.json',
scope=[PEOPLE_API])
credentials = run_flow(flow, storage)
http = credentials.authorize(httplib2.Http())
service = build('people','v1',http=http)
request = service.people().connections().list(resourceName='people/me',
pageSize=500)
analyze_image
api_key = json.load(open('/home/almo/dev/keys/ex1/api_key.json'))['api_key']
service = discovery.build('vision','v1',developerKey=api_key)
service_request = service.images().annotate(body={
'requests': [{
'image': {
'content': image_content.decode('UTF-8')
},
'features': [{
'type': 'LABEL_DETECTION',
'maxResults': 3
}]
}]
}
Data
face; 0,92830354; https://lh3.googleusercontent.com/-c3M1gn6ougg/AAAAAAAAAAI/AAAAAAAAAds/cTIrpGhktfw/photo.jpg?sz=250
text; 0,93046468; https://lh4.googleusercontent.com/-GFVyrVlgMy4/AAAAAAAAAAI/AAAAAAAAABE/u3xVd9eJgf8/photo.jpg?sz=250
font; 0,85384184; https://lh4.googleusercontent.com/-GFVyrVlgMy4/AAAAAAAAAAI/AAAAAAAAABE/u3xVd9eJgf8/photo.jpg?sz=250
line; 0,70535356; https://lh4.googleusercontent.com/-GFVyrVlgMy4/AAAAAAAAAAI/AAAAAAAAABE/u3xVd9eJgf8/photo.jpg?sz=250
eyebrow; 0,98022038; https://lh5.googleusercontent.com/-5c9gdP9nX9M/AAAAAAAAAAI/AAAAAAAAGt4/FoZEEVA8F68/photo.jpg?sz=250
hair; 0,96653992; https://lh5.googleusercontent.com/-5c9gdP9nX9M/AAAAAAAAAAI/AAAAAAAAGt4/FoZEEVA8F68/photo.jpg?sz=250
face; 0,95101357; https://lh5.googleusercontent.com/-5c9gdP9nX9M/AAAAAAAAAAI/AAAAAAAAGt4/FoZEEVA8F68/photo.jpg?sz=250
person; 0,92170084; https://lh4.googleusercontent.com/-yVWpXcqQfXU/AAAAAAAAAAI/AAAAAAAAB5w/rqxRrJHgk_0/photo.jpg?sz=250
news; 0,63342041; https://lh4.googleusercontent.com/-yVWpXcqQfXU/AAAAAAAAAAI/AAAAAAAAB5w/rqxRrJHgk_0/photo.jpg?sz=250
professional; 0,61274487;
https://lh4.googleusercontent.com/-yVWpXcqQfXU/AAAAAAAAAAI/AAAAAAAAB5w/rqxRrJHgk_0/photo.jpg?sz=250
drawer; 0,80023241; https://lh6.googleusercontent.com/-Qf9SSsIUktA/AAAAAAAAAAI/AAAAAAAAABg/u6zPUNXCYFs/photo.jpg?sz=250
furniture; 0,79278195; https://lh6.googleusercontent.com/-Qf9SSsIUktA/AAAAAAAAAAI/AAAAAAAAABg/u6zPUNXCYFs/photo.jpg?sz=250
product; 0,76023591; https://lh6.googleusercontent.com/-Qf9SSsIUktA/AAAAAAAAAAI/AAAAAAAAABg/u6zPUNXCYFs/photo.jpg?sz=250
eyewear; 0,97702742; https://lh3.googleusercontent.com/-ihQNk3ewmzQ/AAAAAAAAAAI/AAAAAAAAAMk/EEEylEyriNE/photo.jpg?sz=250
hair; 0,96766639; https://lh3.googleusercontent.com/-ihQNk3ewmzQ/AAAAAAAAAAI/AAAAAAAAAMk/EEEylEyriNE/photo.jpg?sz=250
sunglasses; 0,96445274; https://lh3.googleusercontent.com/-ihQNk3ewmzQ/AAAAAAAAAAI/AAAAAAAAAMk/EEEylEyriNE/photo.jpg?sz=250
person; 0,92747426; https://lh4.googleusercontent.com/--_BxhkQPYfA/AAAAAAAAAAI/AAAAAAAAACA/1pN6-Chy8EI/photo.jpg?sz=250
person; 0,96007371; https://lh3.googleusercontent.com/-sX8l_lv_-7w/AAAAAAAAAAI/AAAAAAAAAPU/ApQpBMPbcdc/photo.jpg?sz=250
face; 0,95332307; https://lh3.googleusercontent.com/-sX8l_lv_-7w/AAAAAAAAAAI/AAAAAAAAAPU/ApQpBMPbcdc/photo.jpg?sz=250
Raw Data
160 different labels
Max Freq.: 200
Min Freq. : 1
person 200 0,9320951099
hair 140 0,9609928544
face 139 0,9489352931
font 136 0,7606724908
text 130 0,925080287
blue 114 0,9112923658
facial hair 36 0,8802876539
nose 34 0,8859786603
profession 30 0,569073382
hairstyle 25 0,7532089968
cartoon 23 0,8588066957
professional 23 0,6149233535
glasses 20 0,8234816515
facial expression 14 0,9502550086
eyebrow 12 0,9559630675
black and white 11 0,9199305709
eyewear 11 0,9767648145
logo 11 0,7749610755
head 9 0,7432496333
clothing 7 0,9151270214
[{}]
Planning our next
Party
"hair", 0.9559916, "person", 0.94347906, "face", 0.92830354
"text", 0.9304647, "font", 0.85384184, "line", 0.70535356
"eyebrow", 0.9802204, "hair", 0.9665399, "face", 0.9510135
"person", 0.92170084, "news", 0.63342035, "professional", 0.61274487
"drawer", 0.8002325, "furniture", 0.792782, "product", 0.760235
"eyewear", 0.9770274, "hair", 0.9676664, "sunglasses", 0.96445274
"person", 0.92747426,
"https://lh4.googleusercontent.com/--_BxhkQPYfA/AAAAAAAAAAI/AAAAAAAAACA/1pN6-Chy8EI/photo.jpg
?sz=250"
"green", 0.9307698, "text", 0.92834556, "font", 0.8631033
"hair", 0.98155975, "face", 0.95545304, "eyebrow", 0.93590355
"face", 0.9523797, "person", 0.94760686, "hair", 0.94507515
"hair", 0.9731342, "face", 0.94925183, "person", 0.9371813
"hair", 0.94741917, "person", 0.9436425, "hairstyle", 0.7414854
"person", 0.925232, "people", 0.9086431, "male", 0.83032143
"person", 0.95530343, "face", 0.94757956, "nose", 0.86752254
"face", 0.96074444, "hair", 0.9606222, "eyebrow", 0.9451414
"face", 0.9664352, "hair", 0.9561741, "nose", 0.9222636
"phenomenon", 0.94444287, "celestial event", 0.53744316, "aurora", 0.52995497
"face", 0.9625666, "hair", 0.9514838, "facial expression", 0.94977105
"product", 0.80306137, "font", 0.77923214, "logo", 0.69078964
"black and white", 0.9267871, "person", 0.8998944, "photography", 0.8296365
Training
mode!
“invited”, "hair", 0.9559916, "person", 0.94347906, "face", 0.92830354
“excluded”, "text", 0.9304647, "font", 0.85384184, "line", 0.70535356
“excluded”, "eyebrow", 0.9802204, "hair", 0.9665399, "face", 0.9510135
“invited”, "person", 0.92170084, "news", 0.63342035, "professional",
0.61274487
“excluded”, "drawer", 0.8002325, "furniture", 0.792782, "product", 0.760235
“excluded”, "eyewear", 0.9770274, "hair", 0.9676664, "sunglasses", 0.96445274
“excluded”, "green", 0.9307698, "text", 0.92834556, "font", 0.8631033
“excluded”, "hair", 0.98155975, "face", 0.95545304, "eyebrow", 0.93590355
“invited”, "face", 0.9523797, "person", 0.94760686, "hair", 0.94507515
“invited”, "hair", 0.9731342, "face", 0.94925183, "person", 0.9371813
“invited”, "hair", 0.94741917, "person", 0.9436425, "hairstyle", 0.7414854
“invited”, "person", 0.925232, "people", 0.9086431, "male", 0.83032143
“invited”, "person", 0.95530343, "face", 0.94757956, "nose", 0.86752254
“excluded”, "face", 0.96074444, "hair", 0.9606222, "eyebrow", 0.9451414
“excluded”, "face", 0.9664352, "hair", 0.9561741, "nose", 0.9222636
Prediction
Mode!
"hair", 0.9559916, "person", 0.94347906, "face", 0.92830354 “invited”
"text", 0.9304647, "font", 0.85384184, "line", 0.70535356 “excluded”
"eyebrow", 0.9802204, "hair", 0.9665399, "face", 0.9510135 “excluded”
"person", 0.92170084, "news", 0.63342035, "professional", 0.61274487 “invited”
"drawer", 0.8002325, "furniture", 0.792782, "product", 0.760235 “excluded”
"eyewear", 0.9770274, "hair", 0.9676664, "sunglasses", 0.96445274 “excluded”
"green", 0.9307698, "text", 0.92834556, "font", 0.8631033 “excluded”
"hair", 0.98155975, "face", 0.95545304, "eyebrow", 0.93590355 “excluded”
"face", 0.9523797, "person", 0.94760686, "hair", 0.94507515 “invited”
"hair", 0.9731342, "face", 0.94925183, "person", 0.9371813 “invited”
"hair", 0.94741917, "person", 0.9436425, "hairstyle", 0.741485 4 “invited”
"person", 0.925232, "people", 0.9086431, "male", 0.83032143 “invited”
"person", 0.95530343, "face", 0.94757956, "nose", 0.86752254 “invited”
"face", 0.96074444, "hair", 0.9606222, "eyebrow", 0.9451414 “excluded”
"face", 0.9664352, "hair", 0.9561741, "nose", 0.9222636 “excluded”
Beyond AI
Machine Learning find new
ways for our data center to
save energy
Global Fishing
Watch (GFW)
A.I. Experiments
Progress of AI and Machine Learning
https://goo.gl/FWWBv2
Photo by Gerome Viavant on Unsplash
… And the future?
Thank you!
Andres L. Martinez a.k.a almo
Google Developer Relations Manager
@davilagrau

Mais conteúdo relacionado

Mais procurados

GDG DevFest Romania - Architecting for the Google Cloud Platform
GDG DevFest Romania - Architecting for the Google Cloud PlatformGDG DevFest Romania - Architecting for the Google Cloud Platform
GDG DevFest Romania - Architecting for the Google Cloud PlatformMárton Kodok
 
Google Cloud Platform, Compute Engine, and App Engine
Google Cloud Platform, Compute Engine, and App EngineGoogle Cloud Platform, Compute Engine, and App Engine
Google Cloud Platform, Compute Engine, and App EngineCsaba Toth
 
MongoDB Days UK: Run MongoDB on Google Cloud Platform
MongoDB Days UK: Run MongoDB on Google Cloud PlatformMongoDB Days UK: Run MongoDB on Google Cloud Platform
MongoDB Days UK: Run MongoDB on Google Cloud PlatformMongoDB
 
Google Cloud Connect Korea - Sep 2017
Google Cloud Connect Korea - Sep 2017Google Cloud Connect Korea - Sep 2017
Google Cloud Connect Korea - Sep 2017Google Cloud Korea
 
Google Cloud Platform Update
Google Cloud Platform UpdateGoogle Cloud Platform Update
Google Cloud Platform UpdateIdo Green
 
Google Cloud - Scale With A Smile (Dec 2014)
Google Cloud - Scale With A Smile (Dec 2014)Google Cloud - Scale With A Smile (Dec 2014)
Google Cloud - Scale With A Smile (Dec 2014)Ido Green
 
Google Compute Engine Starter Guide
Google Compute Engine Starter GuideGoogle Compute Engine Starter Guide
Google Compute Engine Starter GuideSimon Su
 
Introduction to Google Compute Engine
Introduction to Google Compute EngineIntroduction to Google Compute Engine
Introduction to Google Compute EngineColin Su
 
Getting Started on Google Cloud Platform
Getting Started on Google Cloud PlatformGetting Started on Google Cloud Platform
Getting Started on Google Cloud PlatformAaron Taylor
 
Google Cloud Platform: Prototype ->Production-> Planet scale
Google Cloud Platform: Prototype ->Production-> Planet scaleGoogle Cloud Platform: Prototype ->Production-> Planet scale
Google Cloud Platform: Prototype ->Production-> Planet scaleIdan Tohami
 
Google Cloud Platform - Cloud-Native Roadshow Stuttgart
Google Cloud Platform - Cloud-Native Roadshow StuttgartGoogle Cloud Platform - Cloud-Native Roadshow Stuttgart
Google Cloud Platform - Cloud-Native Roadshow StuttgartVMware Tanzu
 
Google cloud platform
Google cloud platformGoogle cloud platform
Google cloud platformrajdeep
 
Google I/O 2016 Recap - Google Cloud Platform News Update
Google I/O 2016 Recap - Google Cloud Platform News UpdateGoogle I/O 2016 Recap - Google Cloud Platform News Update
Google I/O 2016 Recap - Google Cloud Platform News UpdateSimon Su
 
Introduction to Google Cloud
Introduction to Google CloudIntroduction to Google Cloud
Introduction to Google CloudDSC IEM
 
Cloud computing by Google Cloud Platform - Presentation
Cloud computing by Google Cloud Platform - PresentationCloud computing by Google Cloud Platform - Presentation
Cloud computing by Google Cloud Platform - PresentationTinarivosoaAbaniaina
 
Using Google Compute Engine
Using Google Compute EngineUsing Google Compute Engine
Using Google Compute EngineLynn Langit
 
Google Cloud for Developers - Devfest Manila
Google Cloud for Developers - Devfest ManilaGoogle Cloud for Developers - Devfest Manila
Google Cloud for Developers - Devfest ManilaPatrick Chanezon
 
Understanding cloud with Google Cloud Platform
Understanding cloud with Google Cloud PlatformUnderstanding cloud with Google Cloud Platform
Understanding cloud with Google Cloud PlatformDr. Ketan Parmar
 

Mais procurados (20)

GDG DevFest Romania - Architecting for the Google Cloud Platform
GDG DevFest Romania - Architecting for the Google Cloud PlatformGDG DevFest Romania - Architecting for the Google Cloud Platform
GDG DevFest Romania - Architecting for the Google Cloud Platform
 
Google Cloud Platform, Compute Engine, and App Engine
Google Cloud Platform, Compute Engine, and App EngineGoogle Cloud Platform, Compute Engine, and App Engine
Google Cloud Platform, Compute Engine, and App Engine
 
L2 3.fa19
L2 3.fa19L2 3.fa19
L2 3.fa19
 
MongoDB Days UK: Run MongoDB on Google Cloud Platform
MongoDB Days UK: Run MongoDB on Google Cloud PlatformMongoDB Days UK: Run MongoDB on Google Cloud Platform
MongoDB Days UK: Run MongoDB on Google Cloud Platform
 
Google Cloud Connect Korea - Sep 2017
Google Cloud Connect Korea - Sep 2017Google Cloud Connect Korea - Sep 2017
Google Cloud Connect Korea - Sep 2017
 
Google Cloud Platform Update
Google Cloud Platform UpdateGoogle Cloud Platform Update
Google Cloud Platform Update
 
Google Cloud - Scale With A Smile (Dec 2014)
Google Cloud - Scale With A Smile (Dec 2014)Google Cloud - Scale With A Smile (Dec 2014)
Google Cloud - Scale With A Smile (Dec 2014)
 
Google Compute Engine Starter Guide
Google Compute Engine Starter GuideGoogle Compute Engine Starter Guide
Google Compute Engine Starter Guide
 
Introduction to Google Compute Engine
Introduction to Google Compute EngineIntroduction to Google Compute Engine
Introduction to Google Compute Engine
 
Getting Started on Google Cloud Platform
Getting Started on Google Cloud PlatformGetting Started on Google Cloud Platform
Getting Started on Google Cloud Platform
 
Google Cloud Platform: Prototype ->Production-> Planet scale
Google Cloud Platform: Prototype ->Production-> Planet scaleGoogle Cloud Platform: Prototype ->Production-> Planet scale
Google Cloud Platform: Prototype ->Production-> Planet scale
 
Google Cloud Platform - Cloud-Native Roadshow Stuttgart
Google Cloud Platform - Cloud-Native Roadshow StuttgartGoogle Cloud Platform - Cloud-Native Roadshow Stuttgart
Google Cloud Platform - Cloud-Native Roadshow Stuttgart
 
Google cloud platform
Google cloud platformGoogle cloud platform
Google cloud platform
 
Google I/O 2016 Recap - Google Cloud Platform News Update
Google I/O 2016 Recap - Google Cloud Platform News UpdateGoogle I/O 2016 Recap - Google Cloud Platform News Update
Google I/O 2016 Recap - Google Cloud Platform News Update
 
Data Science on Google Cloud Platform
Data Science on Google Cloud PlatformData Science on Google Cloud Platform
Data Science on Google Cloud Platform
 
Introduction to Google Cloud
Introduction to Google CloudIntroduction to Google Cloud
Introduction to Google Cloud
 
Cloud computing by Google Cloud Platform - Presentation
Cloud computing by Google Cloud Platform - PresentationCloud computing by Google Cloud Platform - Presentation
Cloud computing by Google Cloud Platform - Presentation
 
Using Google Compute Engine
Using Google Compute EngineUsing Google Compute Engine
Using Google Compute Engine
 
Google Cloud for Developers - Devfest Manila
Google Cloud for Developers - Devfest ManilaGoogle Cloud for Developers - Devfest Manila
Google Cloud for Developers - Devfest Manila
 
Understanding cloud with Google Cloud Platform
Understanding cloud with Google Cloud PlatformUnderstanding cloud with Google Cloud Platform
Understanding cloud with Google Cloud Platform
 

Semelhante a Google Cloud: Data Analysis and Machine Learningn Technologies

Track2 02. machine intelligence at google scale google, kaz sato, staff devel...
Track2 02. machine intelligence at google scale google, kaz sato, staff devel...Track2 02. machine intelligence at google scale google, kaz sato, staff devel...
Track2 02. machine intelligence at google scale google, kaz sato, staff devel...양 한빛
 
Kaz Sato, Evangelist, Google at MLconf ATL 2016
Kaz Sato, Evangelist, Google at MLconf ATL 2016Kaz Sato, Evangelist, Google at MLconf ATL 2016
Kaz Sato, Evangelist, Google at MLconf ATL 2016MLconf
 
Google Analytics Konferenz 2018_Machine Learning / AI mit Google_Lukman Ramse...
Google Analytics Konferenz 2018_Machine Learning / AI mit Google_Lukman Ramse...Google Analytics Konferenz 2018_Machine Learning / AI mit Google_Lukman Ramse...
Google Analytics Konferenz 2018_Machine Learning / AI mit Google_Lukman Ramse...e-dialog GmbH
 
Machine Learning for Any Size of Data, Any Type of Data
Machine Learning for Any Size of Data, Any Type of DataMachine Learning for Any Size of Data, Any Type of Data
Machine Learning for Any Size of Data, Any Type of DataDataWorks Summit/Hadoop Summit
 
Cloud Study Jam - 1 (AI/ML & GCP)
Cloud Study Jam - 1 (AI/ML & GCP)Cloud Study Jam - 1 (AI/ML & GCP)
Cloud Study Jam - 1 (AI/ML & GCP)Shiv Prakash
 
[Giovanni Galloro] How to use machine learning on Google Cloud Platform
[Giovanni Galloro] How to use machine learning on Google Cloud Platform[Giovanni Galloro] How to use machine learning on Google Cloud Platform
[Giovanni Galloro] How to use machine learning on Google Cloud PlatformMeetupDataScienceRoma
 
2018 11 14 Artificial Intelligence and Machine Learning in Azure
2018 11 14 Artificial Intelligence and Machine Learning in Azure2018 11 14 Artificial Intelligence and Machine Learning in Azure
2018 11 14 Artificial Intelligence and Machine Learning in AzureBruno Capuano
 
AI and Innovations on AWS
AI and Innovations on AWSAI and Innovations on AWS
AI and Innovations on AWSAdrian Hornsby
 
AI @ Microsoft, How we do it and how you can too!
AI @ Microsoft, How we do it and how you can too!AI @ Microsoft, How we do it and how you can too!
AI @ Microsoft, How we do it and how you can too!Microsoft Tech Community
 
MongoDB World 2019: Gaining ML Insight with Google Vision API and MongoDB
MongoDB World 2019: Gaining ML Insight with Google Vision API and MongoDBMongoDB World 2019: Gaining ML Insight with Google Vision API and MongoDB
MongoDB World 2019: Gaining ML Insight with Google Vision API and MongoDBMongoDB
 
Production ML Systems and Computer Vision with Google Cloud
Production ML Systems and Computer Vision with Google CloudProduction ML Systems and Computer Vision with Google Cloud
Production ML Systems and Computer Vision with Google Cloudgdgsurrey
 
Serverless Computing with Python
Serverless Computing with PythonServerless Computing with Python
Serverless Computing with Pythonwesley chun
 
2018 09 26 CTT .NET User Group - Introduction to Machine Learning.Net and Win...
2018 09 26 CTT .NET User Group - Introduction to Machine Learning.Net and Win...2018 09 26 CTT .NET User Group - Introduction to Machine Learning.Net and Win...
2018 09 26 CTT .NET User Group - Introduction to Machine Learning.Net and Win...Bruno Capuano
 
How Google Cloud Platform can help in the classroom/lab
How Google Cloud Platform can help in the classroom/labHow Google Cloud Platform can help in the classroom/lab
How Google Cloud Platform can help in the classroom/labwesley chun
 
Tour de France Azure PaaS 6/7 Ajouter de l'intelligence
Tour de France Azure PaaS 6/7 Ajouter de l'intelligenceTour de France Azure PaaS 6/7 Ajouter de l'intelligence
Tour de France Azure PaaS 6/7 Ajouter de l'intelligenceAlex Danvy
 
Microsoft AI Platform Overview
Microsoft AI Platform OverviewMicrosoft AI Platform Overview
Microsoft AI Platform OverviewDavid Chou
 
Microsoft AI Platform - AETHER Introduction
Microsoft AI Platform - AETHER IntroductionMicrosoft AI Platform - AETHER Introduction
Microsoft AI Platform - AETHER IntroductionKarthik Murugesan
 

Semelhante a Google Cloud: Data Analysis and Machine Learningn Technologies (20)

Track2 02. machine intelligence at google scale google, kaz sato, staff devel...
Track2 02. machine intelligence at google scale google, kaz sato, staff devel...Track2 02. machine intelligence at google scale google, kaz sato, staff devel...
Track2 02. machine intelligence at google scale google, kaz sato, staff devel...
 
Kaz Sato, Evangelist, Google at MLconf ATL 2016
Kaz Sato, Evangelist, Google at MLconf ATL 2016Kaz Sato, Evangelist, Google at MLconf ATL 2016
Kaz Sato, Evangelist, Google at MLconf ATL 2016
 
Google Analytics Konferenz 2018_Machine Learning / AI mit Google_Lukman Ramse...
Google Analytics Konferenz 2018_Machine Learning / AI mit Google_Lukman Ramse...Google Analytics Konferenz 2018_Machine Learning / AI mit Google_Lukman Ramse...
Google Analytics Konferenz 2018_Machine Learning / AI mit Google_Lukman Ramse...
 
AI at Google (30 min)
AI at Google (30 min)AI at Google (30 min)
AI at Google (30 min)
 
Machine Learning for Any Size of Data, Any Type of Data
Machine Learning for Any Size of Data, Any Type of DataMachine Learning for Any Size of Data, Any Type of Data
Machine Learning for Any Size of Data, Any Type of Data
 
Cloud Study Jam - 1 (AI/ML & GCP)
Cloud Study Jam - 1 (AI/ML & GCP)Cloud Study Jam - 1 (AI/ML & GCP)
Cloud Study Jam - 1 (AI/ML & GCP)
 
AI services in google
AI services in googleAI services in google
AI services in google
 
[Giovanni Galloro] How to use machine learning on Google Cloud Platform
[Giovanni Galloro] How to use machine learning on Google Cloud Platform[Giovanni Galloro] How to use machine learning on Google Cloud Platform
[Giovanni Galloro] How to use machine learning on Google Cloud Platform
 
2018 11 14 Artificial Intelligence and Machine Learning in Azure
2018 11 14 Artificial Intelligence and Machine Learning in Azure2018 11 14 Artificial Intelligence and Machine Learning in Azure
2018 11 14 Artificial Intelligence and Machine Learning in Azure
 
AI and Innovations on AWS
AI and Innovations on AWSAI and Innovations on AWS
AI and Innovations on AWS
 
Democratize ai with google cloud
Democratize ai with google cloudDemocratize ai with google cloud
Democratize ai with google cloud
 
AI @ Microsoft, How we do it and how you can too!
AI @ Microsoft, How we do it and how you can too!AI @ Microsoft, How we do it and how you can too!
AI @ Microsoft, How we do it and how you can too!
 
MongoDB World 2019: Gaining ML Insight with Google Vision API and MongoDB
MongoDB World 2019: Gaining ML Insight with Google Vision API and MongoDBMongoDB World 2019: Gaining ML Insight with Google Vision API and MongoDB
MongoDB World 2019: Gaining ML Insight with Google Vision API and MongoDB
 
Production ML Systems and Computer Vision with Google Cloud
Production ML Systems and Computer Vision with Google CloudProduction ML Systems and Computer Vision with Google Cloud
Production ML Systems and Computer Vision with Google Cloud
 
Serverless Computing with Python
Serverless Computing with PythonServerless Computing with Python
Serverless Computing with Python
 
2018 09 26 CTT .NET User Group - Introduction to Machine Learning.Net and Win...
2018 09 26 CTT .NET User Group - Introduction to Machine Learning.Net and Win...2018 09 26 CTT .NET User Group - Introduction to Machine Learning.Net and Win...
2018 09 26 CTT .NET User Group - Introduction to Machine Learning.Net and Win...
 
How Google Cloud Platform can help in the classroom/lab
How Google Cloud Platform can help in the classroom/labHow Google Cloud Platform can help in the classroom/lab
How Google Cloud Platform can help in the classroom/lab
 
Tour de France Azure PaaS 6/7 Ajouter de l'intelligence
Tour de France Azure PaaS 6/7 Ajouter de l'intelligenceTour de France Azure PaaS 6/7 Ajouter de l'intelligence
Tour de France Azure PaaS 6/7 Ajouter de l'intelligence
 
Microsoft AI Platform Overview
Microsoft AI Platform OverviewMicrosoft AI Platform Overview
Microsoft AI Platform Overview
 
Microsoft AI Platform - AETHER Introduction
Microsoft AI Platform - AETHER IntroductionMicrosoft AI Platform - AETHER Introduction
Microsoft AI Platform - AETHER Introduction
 

Mais de Andrés Leonardo Martinez Ortiz

Fostering Google Software Technologies in Open Digital Ecosystems
Fostering Google Software Technologies in Open Digital EcosystemsFostering Google Software Technologies in Open Digital Ecosystems
Fostering Google Software Technologies in Open Digital EcosystemsAndrés Leonardo Martinez Ortiz
 
Google Cloud Online training resources and certification
Google Cloud Online training resources and certificationGoogle Cloud Online training resources and certification
Google Cloud Online training resources and certificationAndrés Leonardo Martinez Ortiz
 

Mais de Andrés Leonardo Martinez Ortiz (20)

How to plan work for your team
How to plan work for your teamHow to plan work for your team
How to plan work for your team
 
Tensorflow 2.0 and Coral Edge TPU
Tensorflow 2.0 and Coral Edge TPU Tensorflow 2.0 and Coral Edge TPU
Tensorflow 2.0 and Coral Edge TPU
 
Developer journey with classroom
Developer journey with classroomDeveloper journey with classroom
Developer journey with classroom
 
Better code, faster with kubernetes in google cloud
Better code, faster with kubernetes in google cloudBetter code, faster with kubernetes in google cloud
Better code, faster with kubernetes in google cloud
 
Fostering Google Software Technologies in Open Digital Ecosystems
Fostering Google Software Technologies in Open Digital EcosystemsFostering Google Software Technologies in Open Digital Ecosystems
Fostering Google Software Technologies in Open Digital Ecosystems
 
Engineering Machine Learning technologies
Engineering Machine Learning technologiesEngineering Machine Learning technologies
Engineering Machine Learning technologies
 
Google Cloud Online training resources and certification
Google Cloud Online training resources and certificationGoogle Cloud Online training resources and certification
Google Cloud Online training resources and certification
 
The future of conversation ui
The future of conversation uiThe future of conversation ui
The future of conversation ui
 
Clustering tensor flow con kubernetes y raspberry pi
Clustering tensor flow con kubernetes y raspberry piClustering tensor flow con kubernetes y raspberry pi
Clustering tensor flow con kubernetes y raspberry pi
 
Artificial learning machine intelligence
Artificial learning   machine intelligenceArtificial learning   machine intelligence
Artificial learning machine intelligence
 
Curating online content with Google ML API
Curating online content with Google ML API Curating online content with Google ML API
Curating online content with Google ML API
 
Understanding the apps developer environment
Understanding the apps developer environmentUnderstanding the apps developer environment
Understanding the apps developer environment
 
Collaboration! Ramping up the future!
Collaboration! Ramping up the future!Collaboration! Ramping up the future!
Collaboration! Ramping up the future!
 
Firefox OS Innovating Mobile Platforms
Firefox OS Innovating Mobile PlatformsFirefox OS Innovating Mobile Platforms
Firefox OS Innovating Mobile Platforms
 
Wellsprings of innovation
Wellsprings of innovationWellsprings of innovation
Wellsprings of innovation
 
App Circus Developers Economic 2012
App Circus Developers Economic 2012App Circus Developers Economic 2012
App Circus Developers Economic 2012
 
Blue Via Plataforma De Pagos MóViles Y Ap Is De Red
Blue Via   Plataforma De Pagos MóViles Y Ap Is De RedBlue Via   Plataforma De Pagos MóViles Y Ap Is De Red
Blue Via Plataforma De Pagos MóViles Y Ap Is De Red
 
BlueVia 2012
BlueVia 2012BlueVia 2012
BlueVia 2012
 
BlueVia Developer Economics 2011
BlueVia Developer Economics 2011BlueVia Developer Economics 2011
BlueVia Developer Economics 2011
 
Economic Impact For Sm Es Of Cloud Technologies
Economic Impact For Sm Es Of Cloud TechnologiesEconomic Impact For Sm Es Of Cloud Technologies
Economic Impact For Sm Es Of Cloud Technologies
 

Último

Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersNicole Novielli
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxBkGupta21
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
Ryan Mahoney - Will Artificial Intelligence Replace Real Estate Agents
Ryan Mahoney - Will Artificial Intelligence Replace Real Estate AgentsRyan Mahoney - Will Artificial Intelligence Replace Real Estate Agents
Ryan Mahoney - Will Artificial Intelligence Replace Real Estate AgentsRyan Mahoney
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESmohitsingh558521
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 

Último (20)

Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software Developers
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptx
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
Ryan Mahoney - Will Artificial Intelligence Replace Real Estate Agents
Ryan Mahoney - Will Artificial Intelligence Replace Real Estate AgentsRyan Mahoney - Will Artificial Intelligence Replace Real Estate Agents
Ryan Mahoney - Will Artificial Intelligence Replace Real Estate Agents
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 

Google Cloud: Data Analysis and Machine Learningn Technologies

  • 1. Google Data Analysis Technologies Andres L. Martinez a.k.a almo Google Developer Relations Manager @davilagrau
  • 2. Contents ● BigQuery ● Data Analysis ● Machine Learning ● Cloud ML ● Google Assistant ● Cloud ML APIs
  • 4. BigQuery: 100% serverless data warehouse Google BigQuery Fully Managed and Serverless Google Cloud’s Enterprise Data Warehouse for Analytics Petabyte-Scale and Fast Convenience of SQL Encrypted, Durable and Highly Available
  • 5. BigQuery is a great choice because: Near-real time analysis of massive datasets No-ops; Pay for use Durable (replicated), inexpensive storage Immutable audit logs Mashing up different datasets to derive insights
  • 6. 10 B rows Sample query - Processes over 10 billion rows in less than 10 seconds SELECT language, SUM(views) as views FROM wikipedia_benchmark.Wiki10B WHERE regexp_match(title,"G.*o.*o.*g") GROUP by language ORDER by views DESC
  • 7. BigQuery = Massively Parallel Processing query with the petabit network and thousands of servers SQL QueryPetabit Network BigQuery Storage Compute Streaming Ingest Fast Batch Load DataFlow DataProc
  • 8. Load data using bq tool, web UI, or API Create, append or overwrite table CSV, JSON or AVRO format
  • 10. For business analysts Beautiful reports Drive-based collaboration experience No technical expertise required Connects to many sources: BigQuery, Adwords, Google Analytics, Google Sheets, YouTube Analytics, etc.
  • 11. Integrating with Google Data Studio 1 Navigate to DataStudio to create a new dashboard 2 Create a new Data Source 3 Select the type of Data Source to use 4 Authorize
  • 15. Why Machine Learning? ★ Allows to solve problems we don’t have exact solution for. ○ E.g. recommendations, predictions, clustering. ★ Given y = F(X), where we observe y, we can estimate F. ★ Becomes better with more data ○ when hard coded solution usually becomes worse with more code :)
  • 16. Google Products Using Machine Learning
  • 17. Big Data Challenges ★ Variety of data ★ Learning many things at once ★ Small data where matters
  • 18. Why is this time different?Photo by Emily Morter on Unsplash
  • 19. Photo by Sharon Pittaway on Unsplash Innovation
  • 20. Photo by Alex Holyoake on Unsplash
  • 21. Fast{er} Photo by Josh Calabrese on Unsplash
  • 22. Focus on the user
  • 24. Input Out Deep Learning for Perception Tasks
  • 25. Input Out Deep Learning for Perception Tasks
  • 26. Input Out Deep Learning for Perception Tasks
  • 27. Input Out Deep Learning for Perception Tasks
  • 28. Input Out Deep Learning for Perception Tasks
  • 29. Deep Learning combines many components Predictions GoogLeNet Recurrent Neural Network
  • 30. The Machine Learning Spectrum TensorFlow Cloud Machine Learning Machine Learning APIs BYOML skills (Friendly Machine Learning) Pre-packaged ML
  • 31.
  • 32.
  • 33.
  • 35. TensorFlow Core Execution Engine CPU GPU Android iOS ... C++ FrontendPython Frontend ...
  • 36. TensorFlow: Google backed ★ Google supported (growing army of engineers are working on improving it). ★ Used in 100s of products across Google
  • 37. Simple example: How can I use this?
  • 38. Predictive Example Going to deep neural network is easy:
  • 41.
  • 42. Scaling Out TensorFlow scales with number of Machines. You can use Google Cloud ML or Docker containers in VMs. https://arxiv.org/abs/1604.00981
  • 43. TensorFlow Serving: Serving models in production Open Source project. Check it out: http://github.com/tenso rflow/serving
  • 46. Google Home — voice-activated speaker powered The Google Assistant — A conversation between you and Google that helps you get more done in your world. Actions on Google — How developers can extend the assistant (via Conversation Actions)
  • 49. Use machine learning to understand what users are saying
  • 50.
  • 51. “Ok Google, talk to personal chef” Conversation API, Actions SDK Invoke “personal chef” action “Sure, here’s personal chef. Hi, I’m your personal chef, what are you in the mood for?” Speech to Text “What protein would you like to use?” “Well, it’s kind of cold outside, so I’d like something to …” Text to Speech “Sure, here’s your personal chef” Speech to Text, NLP, Knowledge Graph, ML Ranking, User Profile, Text to Speech
  • 52. APIs
  • 53. Confidential & ProprietaryGoogle Cloud Platform 53 So…. Why APIs? { Google Cloud Platform } 1. We want to offer businesses the tools to differentiate by offering a powerful set of APIs that enable apps to see, hear and understand the world 2. Reduce your Time to Market (TMM) when launching your next-generation app 3. Provide you easy access to machine learning technology to give any developer the freedom to work in the language and tools they want 4. Provide virtually limitless scalability to your application without needing to manage back-end servers running deep learning
  • 54. Pre-Trained Machine Learning Models Fully trained ML models from Google Cloud that allow a general developer to take advantage of rich machine learning capabilities with simple REST based services.
  • 55. Introducing Cloud Natural Language API Sentiment analysis and entity recognition for text
  • 56. Confidential & ProprietaryGoogle Cloud Platform 56 Features Extract sentence, identify parts of speech and create dependency parse trees for each sentence Identify entities and label by types such as person, organization, location, events, products and media Understand the overall sentiment of a block of text Access via REST API. Text can be uploaded in the request or integrated with Google Cloud Storage Syntax Analysis Entity Recognition Sentiment Analysis Integrated REST API
  • 57. Cloud Vision API Insight from images with our powerful Cloud Vision API
  • 58. Confidential & ProprietaryGoogle Cloud Platform 58 Faces: Faces, facial landmarks, emotions OCR: Read and extract text, with support for > 10 languages Photo credit Getty Images Label: Detect entities from furniture to transportation Logos: Identify product logos Landmarks & Image Properties Detect landmarks & dominant color of image Safe Search: Detect explicit content - adult, violent, medical and spoof Cloud Vision API Call API from anywhere, with support for embeddable images, and Google Cloud Storage
  • 61.
  • 62.
  • 63. Party planning ● Finding people @Twitter ● Cloud Vision API ● Custom classifier (k-means)
  • 64.
  • 65. Google Cloud Console We need to have access so we can add hash tag to intro slide
  • 67.
  • 69. Main shellplus_contacts = get_plus_contacts() print "Processing %d contacts" % len(plus_contacts) for plus_id in plus_contacts: plus_profile = get_plus_profile(plus_id) image_uri = plus_profile['image']['url'].replace("?sz=50","?sz=250") image_data = analyze_img(image_uri) if image_data is not None: print(image_uri) if 'labelAnnotations' in image_data['responses'][0]: for label in image_data['responses'][0]['labelAnnotations']: print label['description']; label['score']; image_uri
  • 70. get_plus_contacts: oAuth storage = Storage('/home/almo/dev/keys/ex1/oAuth_credentials.dat') credentials = storage.get() if credentials is None or credentials.invalid: PEOPLE_API='https://www.googleapis.com/auth/contacts.readonly' flow = flow_from_clientsecrets('/home/almo/dev/keys/ex1/oAuth_key.json', scope=[PEOPLE_API]) credentials = run_flow(flow, storage) http = credentials.authorize(httplib2.Http()) service = build('people','v1',http=http) request = service.people().connections().list(resourceName='people/me', pageSize=500)
  • 71. analyze_image api_key = json.load(open('/home/almo/dev/keys/ex1/api_key.json'))['api_key'] service = discovery.build('vision','v1',developerKey=api_key) service_request = service.images().annotate(body={ 'requests': [{ 'image': { 'content': image_content.decode('UTF-8') }, 'features': [{ 'type': 'LABEL_DETECTION', 'maxResults': 3 }] }] }
  • 72. Data
  • 73. face; 0,92830354; https://lh3.googleusercontent.com/-c3M1gn6ougg/AAAAAAAAAAI/AAAAAAAAAds/cTIrpGhktfw/photo.jpg?sz=250 text; 0,93046468; https://lh4.googleusercontent.com/-GFVyrVlgMy4/AAAAAAAAAAI/AAAAAAAAABE/u3xVd9eJgf8/photo.jpg?sz=250 font; 0,85384184; https://lh4.googleusercontent.com/-GFVyrVlgMy4/AAAAAAAAAAI/AAAAAAAAABE/u3xVd9eJgf8/photo.jpg?sz=250 line; 0,70535356; https://lh4.googleusercontent.com/-GFVyrVlgMy4/AAAAAAAAAAI/AAAAAAAAABE/u3xVd9eJgf8/photo.jpg?sz=250 eyebrow; 0,98022038; https://lh5.googleusercontent.com/-5c9gdP9nX9M/AAAAAAAAAAI/AAAAAAAAGt4/FoZEEVA8F68/photo.jpg?sz=250 hair; 0,96653992; https://lh5.googleusercontent.com/-5c9gdP9nX9M/AAAAAAAAAAI/AAAAAAAAGt4/FoZEEVA8F68/photo.jpg?sz=250 face; 0,95101357; https://lh5.googleusercontent.com/-5c9gdP9nX9M/AAAAAAAAAAI/AAAAAAAAGt4/FoZEEVA8F68/photo.jpg?sz=250 person; 0,92170084; https://lh4.googleusercontent.com/-yVWpXcqQfXU/AAAAAAAAAAI/AAAAAAAAB5w/rqxRrJHgk_0/photo.jpg?sz=250 news; 0,63342041; https://lh4.googleusercontent.com/-yVWpXcqQfXU/AAAAAAAAAAI/AAAAAAAAB5w/rqxRrJHgk_0/photo.jpg?sz=250 professional; 0,61274487; https://lh4.googleusercontent.com/-yVWpXcqQfXU/AAAAAAAAAAI/AAAAAAAAB5w/rqxRrJHgk_0/photo.jpg?sz=250 drawer; 0,80023241; https://lh6.googleusercontent.com/-Qf9SSsIUktA/AAAAAAAAAAI/AAAAAAAAABg/u6zPUNXCYFs/photo.jpg?sz=250 furniture; 0,79278195; https://lh6.googleusercontent.com/-Qf9SSsIUktA/AAAAAAAAAAI/AAAAAAAAABg/u6zPUNXCYFs/photo.jpg?sz=250 product; 0,76023591; https://lh6.googleusercontent.com/-Qf9SSsIUktA/AAAAAAAAAAI/AAAAAAAAABg/u6zPUNXCYFs/photo.jpg?sz=250 eyewear; 0,97702742; https://lh3.googleusercontent.com/-ihQNk3ewmzQ/AAAAAAAAAAI/AAAAAAAAAMk/EEEylEyriNE/photo.jpg?sz=250 hair; 0,96766639; https://lh3.googleusercontent.com/-ihQNk3ewmzQ/AAAAAAAAAAI/AAAAAAAAAMk/EEEylEyriNE/photo.jpg?sz=250 sunglasses; 0,96445274; https://lh3.googleusercontent.com/-ihQNk3ewmzQ/AAAAAAAAAAI/AAAAAAAAAMk/EEEylEyriNE/photo.jpg?sz=250 person; 0,92747426; https://lh4.googleusercontent.com/--_BxhkQPYfA/AAAAAAAAAAI/AAAAAAAAACA/1pN6-Chy8EI/photo.jpg?sz=250 person; 0,96007371; https://lh3.googleusercontent.com/-sX8l_lv_-7w/AAAAAAAAAAI/AAAAAAAAAPU/ApQpBMPbcdc/photo.jpg?sz=250 face; 0,95332307; https://lh3.googleusercontent.com/-sX8l_lv_-7w/AAAAAAAAAAI/AAAAAAAAAPU/ApQpBMPbcdc/photo.jpg?sz=250 Raw Data
  • 74. 160 different labels Max Freq.: 200 Min Freq. : 1
  • 75. person 200 0,9320951099 hair 140 0,9609928544 face 139 0,9489352931 font 136 0,7606724908 text 130 0,925080287 blue 114 0,9112923658 facial hair 36 0,8802876539 nose 34 0,8859786603 profession 30 0,569073382 hairstyle 25 0,7532089968
  • 76. cartoon 23 0,8588066957 professional 23 0,6149233535 glasses 20 0,8234816515 facial expression 14 0,9502550086 eyebrow 12 0,9559630675 black and white 11 0,9199305709 eyewear 11 0,9767648145 logo 11 0,7749610755 head 9 0,7432496333 clothing 7 0,9151270214
  • 77.
  • 78. [{}]
  • 79.
  • 81. "hair", 0.9559916, "person", 0.94347906, "face", 0.92830354 "text", 0.9304647, "font", 0.85384184, "line", 0.70535356 "eyebrow", 0.9802204, "hair", 0.9665399, "face", 0.9510135 "person", 0.92170084, "news", 0.63342035, "professional", 0.61274487 "drawer", 0.8002325, "furniture", 0.792782, "product", 0.760235 "eyewear", 0.9770274, "hair", 0.9676664, "sunglasses", 0.96445274 "person", 0.92747426, "https://lh4.googleusercontent.com/--_BxhkQPYfA/AAAAAAAAAAI/AAAAAAAAACA/1pN6-Chy8EI/photo.jpg ?sz=250" "green", 0.9307698, "text", 0.92834556, "font", 0.8631033 "hair", 0.98155975, "face", 0.95545304, "eyebrow", 0.93590355 "face", 0.9523797, "person", 0.94760686, "hair", 0.94507515 "hair", 0.9731342, "face", 0.94925183, "person", 0.9371813 "hair", 0.94741917, "person", 0.9436425, "hairstyle", 0.7414854 "person", 0.925232, "people", 0.9086431, "male", 0.83032143 "person", 0.95530343, "face", 0.94757956, "nose", 0.86752254 "face", 0.96074444, "hair", 0.9606222, "eyebrow", 0.9451414 "face", 0.9664352, "hair", 0.9561741, "nose", 0.9222636 "phenomenon", 0.94444287, "celestial event", 0.53744316, "aurora", 0.52995497 "face", 0.9625666, "hair", 0.9514838, "facial expression", 0.94977105 "product", 0.80306137, "font", 0.77923214, "logo", 0.69078964 "black and white", 0.9267871, "person", 0.8998944, "photography", 0.8296365
  • 83. “invited”, "hair", 0.9559916, "person", 0.94347906, "face", 0.92830354 “excluded”, "text", 0.9304647, "font", 0.85384184, "line", 0.70535356 “excluded”, "eyebrow", 0.9802204, "hair", 0.9665399, "face", 0.9510135 “invited”, "person", 0.92170084, "news", 0.63342035, "professional", 0.61274487 “excluded”, "drawer", 0.8002325, "furniture", 0.792782, "product", 0.760235 “excluded”, "eyewear", 0.9770274, "hair", 0.9676664, "sunglasses", 0.96445274 “excluded”, "green", 0.9307698, "text", 0.92834556, "font", 0.8631033 “excluded”, "hair", 0.98155975, "face", 0.95545304, "eyebrow", 0.93590355 “invited”, "face", 0.9523797, "person", 0.94760686, "hair", 0.94507515 “invited”, "hair", 0.9731342, "face", 0.94925183, "person", 0.9371813 “invited”, "hair", 0.94741917, "person", 0.9436425, "hairstyle", 0.7414854 “invited”, "person", 0.925232, "people", 0.9086431, "male", 0.83032143 “invited”, "person", 0.95530343, "face", 0.94757956, "nose", 0.86752254 “excluded”, "face", 0.96074444, "hair", 0.9606222, "eyebrow", 0.9451414 “excluded”, "face", 0.9664352, "hair", 0.9561741, "nose", 0.9222636
  • 85. "hair", 0.9559916, "person", 0.94347906, "face", 0.92830354 “invited” "text", 0.9304647, "font", 0.85384184, "line", 0.70535356 “excluded” "eyebrow", 0.9802204, "hair", 0.9665399, "face", 0.9510135 “excluded” "person", 0.92170084, "news", 0.63342035, "professional", 0.61274487 “invited” "drawer", 0.8002325, "furniture", 0.792782, "product", 0.760235 “excluded” "eyewear", 0.9770274, "hair", 0.9676664, "sunglasses", 0.96445274 “excluded” "green", 0.9307698, "text", 0.92834556, "font", 0.8631033 “excluded” "hair", 0.98155975, "face", 0.95545304, "eyebrow", 0.93590355 “excluded” "face", 0.9523797, "person", 0.94760686, "hair", 0.94507515 “invited” "hair", 0.9731342, "face", 0.94925183, "person", 0.9371813 “invited” "hair", 0.94741917, "person", 0.9436425, "hairstyle", 0.741485 4 “invited” "person", 0.925232, "people", 0.9086431, "male", 0.83032143 “invited” "person", 0.95530343, "face", 0.94757956, "nose", 0.86752254 “invited” "face", 0.96074444, "hair", 0.9606222, "eyebrow", 0.9451414 “excluded” "face", 0.9664352, "hair", 0.9561741, "nose", 0.9222636 “excluded”
  • 86.
  • 87.
  • 89. Machine Learning find new ways for our data center to save energy
  • 92. Progress of AI and Machine Learning https://goo.gl/FWWBv2
  • 93. Photo by Gerome Viavant on Unsplash … And the future?
  • 94. Thank you! Andres L. Martinez a.k.a almo Google Developer Relations Manager @davilagrau