How Google Cloud Platform can help in the classroom/lab
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The document outlines how the Google Cloud Platform (GCP) can support educators and researchers in higher education through various cloud computing services and tools. It emphasizes the importance of integrating cloud technologies in curriculum design to prepare students for a cloud-ready workforce while providing hands-on experience in cloud computing. The GCP offers a wide range of resources, including virtual machines, machine learning capabilities, and storage options, as well as opportunities for education grants to facilitate cloud usage without personal expenses.
![Machine Learning: Cloud Natural Language
Google Cloud Natural Language API
reveals the structure & meaning
of text; also performs content
classification and sentiment
analysis; multi-lingual
cloud.google.com/language
Simple sentiment & classification analysis
TEXT = '''Google, headquartered in Mountain View, unveiled the new
Android phone at the Consumer Electronics Show. Sundar Pichai said
in his keynote that users love their new Android phones.'''
print('TEXT:', TEXT)
data = {'type': 'PLAIN_TEXT', 'content': TEXT}
NL = discovery.build('language', 'v1', developerKey=API_KEY)
# sentiment analysis
sent = NL.documents().analyzeSentiment(
body={'document': data}).execute().get('documentSentiment')
print('nSENTIMENT: score (%s), magnitude (%s)' % (sent['score'], sent['magnitude']))
# content classification
print('nCATEGORIES:')
cats = NL.documents().classifyText(body={'document': data}).execute().get('categories')
for cat in cats:
print('* %s (%s)' % (cat['name'][1:], cat['confidence']))](https://image.slidesharecdn.com/gcpcanhelp-2-191018141530/85/How-Google-Cloud-Platform-can-help-in-the-classroom-lab-39-320.jpg)
![Text-to-Speech: synthsizing audio text
# request body (with text body using 16-bit linear PCM audio encoding)
body = {
'input': {'text': text},
'voice': {
'languageCode': 'en-US',
'ssmlGender': 'FEMALE',
},
'audioConfig': {'audioEncoding': 'LINEAR16'},
}
# call Text-to-Speech API to synthesize text (write to text.wav file)
T2S = discovery.build('texttospeech', 'v1', developerKey=API_KEY)
audio = T2S.text().synthesize(body=body).execute().get('audioContent')
with open('text.wav', 'wb') as f:
f.write(base64.b64decode(audio))
Speech-to-Text: transcribing audio text
# request body (16-bit linear PCM audio content, i.e., from text.wav)
body = {
'audio': {'content': audio},
'config': {
'languageCode': 'en-US',
'encoding': 'LINEAR16',
},
}
# call Speech-to-Text API to recognize text
S2T = discovery.build('speech', 'v1', developerKey=API_KEY)
rsp = S2T.speech().recognize(
body=body).execute().get('results')[0]['alternatives'][0]
print('** %.2f%% confident of this transcript:n%r' % (
rsp['confidence']*100., rsp['transcript']))](https://image.slidesharecdn.com/gcpcanhelp-2-191018141530/85/How-Google-Cloud-Platform-can-help-in-the-classroom-lab-41-320.jpg)
![Video intelligence: make videos searchable
# request body (single payload, base64 binary video)
body = {
"inputContent": video,
"features": ['LABEL_DETECTION', 'SPEECH_TRANSCRIPTION'],
"videoContext": {"speechTranscriptionConfig": {"languageCode": 'en-US'}},
}
# perform video shot analysis followed by speech analysis
VINTEL = discovery.build('videointelligence', 'v1', developerKey=API_KEY)
resource = VINTEL.videos().annotate(body=body).execute().get('name')
while True:
results = VINTEL.operations().get(name=resource).execute()
if results.get('done'):
break
time.sleep(random.randrange(8)) # expo-backoff probably better
Video intelligence: make videos searchable
# display shot labels followed by speech transcription
for labels in results['response']['annotationResults']:
if 'shotLabelAnnotations' in labels:
print('n** Video shot analysis labeling')
for shot in labels['shotLabelAnnotations']:
seg = shot['segments'][0]
print(' - %s (%.2f%%)' % (
shot['entity']['description'], seg['confidence']*100.))
if 'speechTranscriptions' in labels:
print('** Speech transcription')
speech = labels['speechTranscriptions'][0]['alternatives'][0]
print(' - %r (%.2f%%)' % (
speech['transcript'], speech['confidence']*100.))](https://image.slidesharecdn.com/gcpcanhelp-2-191018141530/85/How-Google-Cloud-Platform-can-help-in-the-classroom-lab-43-320.jpg)
![Translate some texts: “Hello World!”
const {Translate} = require('@google-cloud/translate');
const translate = new Translate({projectConfig});
const text = 'Hello World!'; // Text to translate
const target = 'ru'; // Target language
// Translates some text into Russian
const translation = await translate.translate(text, {from: 'en', to:
target}));
// Translation: Привет, мир!
console.log('Translation:', translation[0]);
Machine Learning: AutoML
AutoML: a suite of cloud APIs for
developers with limited machine
learning expertise; chooses the best
models & allows for further training
of those models for your data
(Translation, Vision, Natural Language,
Video Intelligence, Tables)
cloud.google.com/automl
cloud.google.com/automl-tables](https://image.slidesharecdn.com/gcpcanhelp-2-191018141530/85/How-Google-Cloud-Platform-can-help-in-the-classroom-lab-45-320.jpg)
![BigQuery: querying Shakespeare words
TITLE = "The top 10 most common words in all of Shakespeare's works"
QUERY = '''
SELECT LOWER(word) AS word, sum(word_count) AS count
FROM [bigquery-public-data:samples.shakespeare]
GROUP BY word ORDER BY count DESC LIMIT 10
'''
rsp = BQ.query(body={'query': QUERY}, projectId=PROJ_ID).execute()
print('n*** Results for %r:n' % TITLE)
for col in rsp['schema']['fields']: # HEADERS
print(col['name'].upper(), end='t')
print()
for row in rsp['rows']: # DATA
for col in row['f']:
print(col['v'], end='t')
print()
Top 10 most common Shakespeare words
$ python bq_shake.py
*** Results for "The most common words in all of Shakespeare's works":
WORD COUNT
the 29801
and 27529
i 21029
to 20957
of 18514
a 15370
you 14010
my 12936
in 11722
that 11519](https://image.slidesharecdn.com/gcpcanhelp-2-191018141530/85/How-Google-Cloud-Platform-can-help-in-the-classroom-lab-49-320.jpg)
![Other Google APIs & platforms
● Firebase (mobile development platform + RT DB)
○ firebase.google.com
● Google Data Studio (data visualization, dashboards, etc.)
○ marketingplatform.google.com/about/data-studio
● Actions on Google/Assistant/DialogFlow (voice apps)
○ developers.google.com/actions
● YouTube (Data, Analytics, and Livestreaming APIs)
○ developers.google.com/youtube
● Google Maps (Maps, Routes, and Places APIs)
○ developers.google.com/maps
● Flutter (native apps [Android, iOS, web] w/1 code base[!])
○ flutter.dev
6 Wrap-up
Summary & resources for faculty, lecturers, researchers](https://image.slidesharecdn.com/gcpcanhelp-2-191018141530/85/How-Google-Cloud-Platform-can-help-in-the-classroom-lab-51-320.jpg)
How Google Cloud Platform can help in the classroom/lab
- 1. Google Cloud Platform How GCP can help in the classroom/lab Wesley Chun Developer Advocate, Google Adjunct CS Faculty, Foothill College Laurie White CS Professor Emeritus, Mercer University GCP Developer Relations, Google G Suite Dev Show goo.gl/JpBQ40 About the speaker Developer Advocate, Google ● Mission: enable current and future developers everywhere to be successful using Google Cloud and other Google developer tools & APIs ● Videos: host of the G Suite Dev Show on YouTube ● Blogs: developers.googleblog.com & gsuite-developers.googleblog.com ● Twitters: @wescpy, @GoogleDevs, @GSuiteDevs Previous experience / background ● Software engineer & architect for 20+ years ● One of the original Yahoo!Mail engineers ● Author of bestselling "Core Python" books (corepython.com) ● Technical trainer, teacher, instructor since 1983 (Computer Science, C, Linux, Python) ● Fellow of the Python Software Foundation ● AB (Math/CS) & CMP (Music/Piano), UC Berkeley and MSCS, UC Santa Barbara ● Adjunct Computer Science Faculty, Foothill College (Silicon Valley)
- 2. Why and Agenda ● Cloud has taken industry by storm (all?) ● Not enough cloud computing in higher-ed curriculum ● How the cloud can help with your courses/research ● You leave with hands-on experience using cloud computing services ● Help prep next-generation cloud-ready workforce ● We do many student events; faculty/staff: "What about us?!?" Cloud computing overview 1 Introduction to Google Cloud 2 Hosting your code (+storage options) 3 Machine Learning 4 5 Other APIs to consider 6 Wrap-up Cloud computing overview All you need to know about the cloud1
- 3. What is cloud computing? spar What is Google Cloud Platform? Getting things done using someone else’s computers, especially where someone else worries about maintenance, provisioning, system administration, security, networking, failure recover, etc.
- 4. Google Compute Engine, Google Cloud Storage AWS EC2 & S3; Rackspace; Joyent Cloud service levels/"pillars" SaaS Software as a Service PaaS Platform as a Service IaaS Infrastructure as a Service Google BigQuery, Cloud SQL, Cloud Datastore, NL, Vision, Pub/Sub AWS Kinesis, RDS; Windows Azure SQL, Docker Google Apps Script, App Maker Salesforce1/force.com G Suite (Google Apps) Yahoo!Mail, Hotmail, Salesforce, Netsuite Google App Engine, Cloud Functions Heroku, Cloud Foundry, Engine Yard, AWS Lambda Summary of responsibility SaaS Software as a Service Applications Data Runtime Middleware OS Virtualization Servers Storage Networking Applications Data Runtime Middleware OS Virtualization Servers Storage Networking IaaS Infrastructure as a Service Applications Data Runtime Middleware OS Virtualization Servers Storage Networking PaaS Platform as a Service Managed by YOU Managed by cloud vendor Applications Data Runtime Middleware OS Virtualization Servers Storage Networking on-prem all you, no cloud
- 5. Theme - Spec/Reqs Logistics - Design app Space - Provision HW Food - Build & serve app Manage - Manage app on-prem (DIY) IaaS (Compute Engine) PaaS (App Engine/GCF) SaaS (G Suite) Pick theme Plan party Find space Cook On-call Pick theme Plan party Rent hall Cook On-call Pick theme Plan party Rent hall Hire Caterer Hire manager Pick theme Hire planner Rent hall Hire caterer Hire manager Imagine you’re hosting a party... How can Google Cloud help in higher ed? ● What can we provide faculty, researchers, IT staff, students? ○ Virtual machines, GPUs, and variety of data storage ○ Ability to craft & design your own network/subnet ○ Pre-trained machine learning models ○ Container-hosting, ML build & deploy infrastructure ○ Serverless compute & data services ○ Additional or emergency compute & storage capacity ○ Productivity tools students already use (G Suite) ○ Education grants (use our cloud w/o personal credit cards)
- 6. Possible courses using Google Cloud ● Most likely ○ Software Engineering, Cloud Computing, Machine Learning/AI ○ Networking (OS), Database (RDBMS, NoSQL), System Admin/DevOps ○ Web app courses, mobile app courses, business applications ○ Any class where student coursework requires coding ● Possible ○ Data Structures & Algorithms, Compilers, Numerical Analysis (math) ○ Lower division undergrad intro to CS, independent study ○ (outside Eng) Geography, Library & Information Studies, b-school ○ Capstone projects, university entrepreneurship center projects 2 Introduction to Google Cloud GCP and G Suite tools & APIs
- 7. YOUR NEXT APP? google.com/datacenter Eight products with 1B+ (monthly) users each
- 9. What is Google Cloud Platform? GCP lets you build & host code (web apps, mobile backends, web services, containers), store & analyze data, and much more, all on Google’s highly-scalable & reliable computing infrastructure Compute Big Data BigQuery Cloud Dataflow Cloud Dataproc Cloud Datalab Cloud Pub/Sub Genomics Cloud AI/ML Cloud Machine Learning Engine Cloud Vision Cloud Speech-to-Text Cloud Natural Language Cloud Translation Cloud Jobs Cloud Dataprep Cloud Video Intelligence Advanced Solutions Lab Compute Engine App Engine Kubernetes Engine GPU Cloud Functions Identity & Security Cloud IAM Cloud Resource Manager Cloud Security Scanner Key Management Service BeyondCorp Data Loss Prevention Identity-Aware Proxy Security Key Enforcement Cloud AutoML Cloud Text-to-Speech Cloud TPU Dialogflow Enterprise Edition Cloud Composer Cloud Security Command Center Cloud Run AutoML Tables
- 10. Developer Tools Cloud SDK Cloud Source Repositories Maven App Engine Plugin Cloud Tools for IntelliJ Cloud Tools for PowerShell Cloud Tools for Visual Studio Container Registry Cloud Tools for Eclipse Cloud Build API Platform & Ecosystems API Analytics API Monetization Apigee API Platform Apigee Sense Cloud Endpoints Developer Portal Gradle App Engine Plugin IDE plugins Internet of Things Cloud IoT Core Data (storage, databases, transfer) Cloud Storage Cloud Bigtable Cloud Datastore Cloud SQL Cloud Spanner Persistent Disk Cloud Memorystore Cloud Filestore Cloud Test Lab Cloud IoT Edge Container- Optimized OS Healthcare API Cloud Code Cloud Firestore Transfer Appliance Networking Virtual Private Cloud Cloud Load Balancing Cloud CDN Dedicated Interconnect Cloud DNS Cloud Network Cloud External IP Addresses Cloud Firewall Rules Cloud Routes Cloud VPN Management Tools Stackdriver Monitoring Logging Error Reporting Trace Debugger Cloud Deployment Manager Cloud Console Cloud Shell Cloud Mobile App Cloud Billing API Cloud APIs Cloud Router Partner Interconnect Cloud Armor Standard Network Tier Premium Network Tier Profiler GCP products and services without a unique icon have a generic hexagon. Cloud NAT
- 11. Cloud/GCP console console.cloud.google.com ● Hub of all developer activity ● Applications == projects ○ New project for new apps ○ Projects have a billing acct ● Manage billing accounts ○ Financial instrument required ○ Personal or corporate credit cards, Free Trial, and education grants ● Access GCP product settings ● Manage users & security ● Manage APIs in devconsole Navigating the Cloud console
- 12. ● View application statistics ● En-/disable Google APIs ● Obtain application credentials Using Google APIs goo.gl/RbyTFD API manager aka Developers Console (devconsole) console.developers.google.com OAuth2 or API key HTTP-based REST APIs 1 HTTP 2 Google APIs request-response workflow ● Application makes request ● Request received by service ● Process data, return response ● Results sent to application (typical client-server model)
- 13. What is Google Cloud Platform?some Google Cloud Platform products (tl;dr) Compute Big Data BigQuery Cloud Dataflow Cloud Dataproc Cloud Datalab Cloud Pub/Sub Genomics Storage & Databases Cloud Storage Cloud Bigtable Cloud Datastore Cloud SQL Cloud Spanner Persistent Disk Machine Learning Cloud ML Engine Cloud Vision API Cloud Speech APIs Cloud Natural Language API Cloud Translation API Cloud Jobs API Data Studio Cloud Dataprep Cloud Video Intelligence API AutoML auite Compute Engine App Engine Kubernete s Engine GPU Cloud Functions Container- Optimized OS Identity & Security Cloud IAM Cloud Resource Manager Cloud Security Scanner Key Management Service BeyondCorp Data Loss Prevention API Identity-Aware Proxy Security Key Enforcement Transfer Appliance Cloud Firestore Internet of Things Cloud IoT Core QwikLabs codelabs ● Codelabs == self-paced, hands-on tutorials ● "Quests" == group of codelabs arranged in a "learning path" ● No Google account ( provisioned on-the-fly) ● Apply for QwikLabs coupons at cloud.google.com/edu ○ Individual grant 200 credits … OR ○ Request 5000 credits for use in courses ● Special for attendees of this event! ○ Credits (15 credits) to complete a pair of quests: ■ GCP Essentials and one other; recommended: "Baseline" series ○ Sign up today at bit.ly/2kJL39T … link expires 2019 Dec 20
- 14. 3 Hosting your code Compute & storage options Compute (running code)
- 15. Running Code: Compute Engine > Google Compute Engine delivers configurable virtual machines of all shapes and sizes, from "micro" to 416 vCPUs, 11.75 TB RAM, 64 TB HDD or SSD disk (Debian, CentOS, CoreOS, SUSE, Red Hat Enterprise Linux, Ubuntu, FreeBSD; Windows Server 2008 R2, 2012 R2, 2016) cloud.google.com/compute Classroom use Provide servers on demand for: ● Web dev classes ● DevOps/SysAdmin courses ● R-studio and similar projects ● Networking classes ● Linux or OS courses How? Find "Compute Engine" in left-nav of GCP cloud console:
- 16. Compute Engine exercise Let's create a free* VM now! * users get one free "micro" VM/month (see cloud.google.com/free) (If you forget all this later, go to gcplab.me and search for "Creating a Virtual Machine.") > Google Compute Engine configurable VMs of all shapes & sizes, from "micro" to 416 vCPUs, 11.75 TB RAM, 64 TB HDD/SSD plus Google Cloud Storage for blobs/cloud data lake (Debian, CentOS, CoreOS, SUSE, Red Hat Enterprise Linux, Ubuntu, FreeBSD; Windows Server 2008 R2, 2012 R2, 2016) cloud.google.com/compute cloud.google.com/storage Yeah, we got VMs & big disk… but why*?
- 17. Serverless: what & why ● What is serverless? ○ Misnomer ○ "No worries" ○ Developers focus on writing code & solving business problems* ● Why serverless? ○ Fastest growing segment of cloud... per analyst research*: ■ $1.9B (2016) and $4.25B (2018) ⇒ $7.7B (2021) and $14.93B (2023) ○ What if you go viral? Autoscaling: your new best friend ○ What if you don't? Code not running? You're not paying. * in USD; source:Forbes (May 2018), MarketsandMarkets™ & CB Insights (Aug 2018) Google Compute Engine, Google Cloud Storage AWS EC2 & S3; Rackspace; Joyent SaaS Software as a Service PaaS Platform as a Service IaaS Infrastructure as a Service G Suite (Google Apps) Yahoo!Mail, Hotmail, Salesforce, Netsuite Google App Engine, Cloud Functions Heroku, Cloud Foundry, Engine Yard, AWS Lambda Google BigQuery, Cloud SQL, Cloud Datastore, NL, Vision, Pub/Sub AWS Kinesis, RDS; Windows Azure SQL, Docker Serverless: PaaS-y compute/processing Google Apps Script, App Maker Salesforce1/force.com
- 18. Why does App Engine exist? ● Focus on app not DevOps ○ Web app ○ Mobile backend ○ Cloud service ● Enhance productivity ● Deploy globally ● Fully-managed ● Auto-scaling ● Pay-per-use ● Familiar languages App Engine to the rescue!! ● Focus on app not DevOps ● Enhance productivity ● Deploy globally ● Fully-managed ● Auto-scaling ● Pay-per-use ● Familiar standard runtimes ● 2nd gen std platforms ○ Python 3.7 ○ Java 8, 11 ○ PHP 7.2 ○ Go 1.11 ○ JS/Node.js 8, 10 ○ Ruby 2.5
- 19. Hello World (3 files: Python "MVP") app.yaml runtime: python37 main.py from flask import Flask app = Flask(__name__) @app.route('/') def hello(): return 'Hello World!' requirements.txt Flask==1.0.2 Deploy: $ gcloud app deploy Access globally: https://PROJECT_ID.appspot.com Open source repo at github.com/GoogleCloudPlatform/python-docs-samples/ tree/master/appengine/standard_python37/hello_world App Engine exercise Python/Flask QuickStart tutorial (also Java, Node.js, PHP, Go, Ruby)
- 20. Running Code: Cloud Functions Don't have an entire app? Just want to deploy small microservices or "RPCs" online globally? That's what Google Cloud Functions are for! (+Firebase version for mobile apps) cloud.google.com/functions firebase.google.com/products/functions Why does Cloud Functions exist? ● Don't have entire app? ○ No framework "overhead" (LAMP, MEAN...) ○ Deploy microservices ● Event-driven ○ Triggered via HTTP or background events ■ Pub/Sub, Cloud Storage, Firebase, etc. ○ Auto-scaling & highly-available; pay per use ● Flexible development environment ○ Cmd-line or developer console ● Cloud Functions for Firebase ○ Mobile app use-cases ● Available runtimes ○ JS/Node.js 6, 8, 10 ○ Python 3.7 ○ Go 1.11, 1.12 ○ Java 8
- 21. main.py def hello_world(request): return 'Hello World!' Deploy: $ gcloud functions deploy hello --runtime python37 --trigger-http Access globally (curl): curl -X POST https://GCP_REGION-PROJECT_ID.cloudfunctions.net/hello -H "Content-Type:application/json" Access globally (browser): GCP_REGION-PROJECT_ID.cloudfunctions.net/hello Hello World (Python "MVP") No cmd-line access? Use in-browser dev environment! ● setup ● code ● deploy ● test
- 22. Cloud Functions exercise Python QuickStart tutorial (also in Node.js & Go) Running Code: Cloud Run Got a containerized app? Want its flexibility along with the convenience of serverless that's fully-managed plus auto-scales? Google Cloud Run is exactly what you're looking for! cloud.google.com/run
- 23. Code, build, deploy .js .rb .go .sh.py ... ● Any language, library, binary ○ HTTP port, stateless ● Bundle into container ○ Build w/Docker OR ○ Google Cloud Build ○ Image ⇒ Container Registry ● Deploy to Cloud Run (managed or GKE) StateHTTP https://yourservice.run.app Hello World (3 files: Python "MVP") Dockerfile FROM python:3.7 ENV APP_HOME /app ENV TARGET MHacks2019 WORKDIR $APP_HOME COPY . . RUN pip install Flask gunicorn CMD exec gunicorn --bind :$PORT --workers 1 --threads 8 app:app cloud.google.com/run/docs/quickstarts/build-and-deploy or github.com/knative/docs/tree/master/docs/serving/samples/h ello-world/helloworld-python .dockerignore Dockerfile README.md *.pyc *.pyo *.pyd __pycache__
- 24. Hello World (3 files: Python "MVP") app.py import os from flask import Flask app = Flask(__name__) @app.route('/') def hello_world(): target = os.environ.get('TARGET', 'World') return 'Hello {}!'.format(target) if __name__ == '__main__': app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8080))) Hello World (3 files: Python "MVP") Build (think docker build): $ gcloud builds submit --tag gcr.io/PROJ_ID/CONT_NAME Deploy (think docker push): $ gcloud beta run deploy --image gcr.io/PROJ_ID/CONT_NAME --platform managed Access globally: https://CONT_NAME-RANDOM_HASH-uc.a.run.app
- 25. Cloud Run exercise Python QuickStart tutorial (also in Node.js, Java, Ruby, C#/.NET, PHP, Dart, Kotlin, Go, Rust) Storage (where to put your data)
- 26. Storing Data: Cloud Storage & Cloud Filestore cloud.google.com/storage cloud.google.com/filestore Storing Data: Cloud SQL SQL servers in the cloud High-performance, fully-managed 600MB to 416GB RAM; up to 64 vCPUs Up to 10 TB storage; 40,000 IOPS Types: MySQL Postgres SQLServer (2019) cloud.google.com/sql
- 27. Storing Data: Cloud Datastore Cloud Datastore: a fully- managed, highly-scalable NoSQL database for your web and mobile applications cloud.google.com/datastore Storing Data: Firebase Firebase data is stored as JSON & synchronized in real-time to every connected client; other tools + FB == v2 mobile development platform firebase.google.com
- 28. Storing Data: Cloud Firestore The best of both worlds: the next generation of Cloud Datastore (w/product rebrand) plus features from the Firebase realtime database cloud.google.com/firestore ● Ordinary database - explicitiy query database for new updates (but when?) ● Realtime database - automatically receive deltas when database updated (setup client listener object) ● Highly scalable database ● Multi-regional data replication ● Strong consistency (vs. eventual consistency): read your writes! Cloud Firestore key features
- 29. Cloud Firestore data model collections subcollections Cloud Firestore easy querying
- 30. Cloud Firestore demo bit.ly/firestore-game 4 Machine Learning Access powerful data analysis tools via APIs; ML expertise not required
- 31. Puppy or Muffin?
- 32. Machine learning is learning from rules plus experience.
- 34. Google Translate
- 35. Google Photos Did you ever stop to notice this app has a search bar?!? Machine Learning (analyze your data)
- 36. GCP Machine Learning APIs ● Gain insights from data using GCP's pre-trained machine learning models ● Leverage the same technology as Google Translate, Photos, and Assistant ● Requires ZERO prior knowledge of ML ● If you can call an API, you can use AI/ML! Vision Video Intelligence Speech (S2T & T2S) Natural Language Translation Machine Learning: Cloud Vision Google Cloud Vision API enables developers to extract metadata & understand the content of an image cloud.google.com/vision
- 37. from google.cloud import vision image_uri = 'gs://cloud-samples-data/vision/using_curl/shanghai.jpeg' client = vision.ImageAnnotatorClient() image = vision.types.Image() image.source.image_uri = image_uri response = client.label_detection(image=image) print('Labels (and confidence score):') print('=' * 30) for label in response.label_annotations: print(f'{label.description} ({label.score*100.:.2f}%)') Vision: label annotation/object detection $ python3 label-detect.py Labels (and confidence score): ============================== People (95.05%) Street (89.12%) Mode of transport (89.09%) Transport (85.13%) Vehicle (84.69%) Snapshot (84.11%) Urban area (80.29%) Infrastructure (73.14%) Road (72.74%) Pedestrian (68.90%) Vision: label annotation/object detection codelabs.developers.google.com/codelabs/cloud-vision-api-python#6
- 38. Cloud Vision demo quickdraw.withgoogle.com Cloud Vision exercise gcplab.me (search for "Vision API with Python")
- 39. Machine Learning: Cloud Natural Language Google Cloud Natural Language API reveals the structure & meaning of text; also performs content classification and sentiment analysis; multi-lingual cloud.google.com/language Simple sentiment & classification analysis TEXT = '''Google, headquartered in Mountain View, unveiled the new Android phone at the Consumer Electronics Show. Sundar Pichai said in his keynote that users love their new Android phones.''' print('TEXT:', TEXT) data = {'type': 'PLAIN_TEXT', 'content': TEXT} NL = discovery.build('language', 'v1', developerKey=API_KEY) # sentiment analysis sent = NL.documents().analyzeSentiment( body={'document': data}).execute().get('documentSentiment') print('nSENTIMENT: score (%s), magnitude (%s)' % (sent['score'], sent['magnitude'])) # content classification print('nCATEGORIES:') cats = NL.documents().classifyText(body={'document': data}).execute().get('categories') for cat in cats: print('* %s (%s)' % (cat['name'][1:], cat['confidence']))
- 40. Simple sentiment & classification analysis $ python nl_sent_simple.py TEXT: Google, headquartered in Mountain View, unveiled the new Android phone at the Consumer Electronics Show. Sundar Pichai said in his keynote that users love their new Android phones. SENTIMENT: score (0.3), magnitude (0.6) CATEGORIES: * Internet & Telecom (0.76) * Computers & Electronics (0.64) * News (0.56) Machine Learning: Cloud Speech Google Cloud Speech APIs enable developers to convert speech-to-text and vice versa cloud.google.com/speech cloud.google.com/text-to-speech
- 41. Text-to-Speech: synthsizing audio text # request body (with text body using 16-bit linear PCM audio encoding) body = { 'input': {'text': text}, 'voice': { 'languageCode': 'en-US', 'ssmlGender': 'FEMALE', }, 'audioConfig': {'audioEncoding': 'LINEAR16'}, } # call Text-to-Speech API to synthesize text (write to text.wav file) T2S = discovery.build('texttospeech', 'v1', developerKey=API_KEY) audio = T2S.text().synthesize(body=body).execute().get('audioContent') with open('text.wav', 'wb') as f: f.write(base64.b64decode(audio)) Speech-to-Text: transcribing audio text # request body (16-bit linear PCM audio content, i.e., from text.wav) body = { 'audio': {'content': audio}, 'config': { 'languageCode': 'en-US', 'encoding': 'LINEAR16', }, } # call Speech-to-Text API to recognize text S2T = discovery.build('speech', 'v1', developerKey=API_KEY) rsp = S2T.speech().recognize( body=body).execute().get('results')[0]['alternatives'][0] print('** %.2f%% confident of this transcript:n%r' % ( rsp['confidence']*100., rsp['transcript']))
- 42. Speech-to-Text: transcribing audio text $ python s2t_demo.py ** 92.03% confident of this transcript: 'Google headquarters in Mountain View unveiled the new Android phone at the Consumer Electronics Show Sundar pichai said in his keynote that users love their new Android phones' Machine Learning: Cloud Video Intelligence Google Cloud Video Intelligence API makes videos searchable, and discoverable, by extracting metadata. Other features: object tracking, shot change detection, and text detection cloud.google.com/video-intelligence
- 43. Video intelligence: make videos searchable # request body (single payload, base64 binary video) body = { "inputContent": video, "features": ['LABEL_DETECTION', 'SPEECH_TRANSCRIPTION'], "videoContext": {"speechTranscriptionConfig": {"languageCode": 'en-US'}}, } # perform video shot analysis followed by speech analysis VINTEL = discovery.build('videointelligence', 'v1', developerKey=API_KEY) resource = VINTEL.videos().annotate(body=body).execute().get('name') while True: results = VINTEL.operations().get(name=resource).execute() if results.get('done'): break time.sleep(random.randrange(8)) # expo-backoff probably better Video intelligence: make videos searchable # display shot labels followed by speech transcription for labels in results['response']['annotationResults']: if 'shotLabelAnnotations' in labels: print('n** Video shot analysis labeling') for shot in labels['shotLabelAnnotations']: seg = shot['segments'][0] print(' - %s (%.2f%%)' % ( shot['entity']['description'], seg['confidence']*100.)) if 'speechTranscriptions' in labels: print('** Speech transcription') speech = labels['speechTranscriptions'][0]['alternatives'][0] print(' - %r (%.2f%%)' % ( speech['transcript'], speech['confidence']*100.))
- 44. Video intelligence: make videos searchable $ python3 vid_demo.py you-need-a-hug.mp4 ** Video shot analysis labeling - vacation (30.62%) - fun (61.53%) - interaction (38.93%) - summer (57.10%) ** Speech transcription - 'you need a hug come here' (79.27%) Machine Learning: Cloud Translation Access Google Translate programmatically through this API; translate an arbitrary string into any supported language using state-of-the-art Neural Machine Translation cloud.google.com/translate
- 45. Translate some texts: “Hello World!” const {Translate} = require('@google-cloud/translate'); const translate = new Translate({projectConfig}); const text = 'Hello World!'; // Text to translate const target = 'ru'; // Target language // Translates some text into Russian const translation = await translate.translate(text, {from: 'en', to: target})); // Translation: Привет, мир! console.log('Translation:', translation[0]); Machine Learning: AutoML AutoML: a suite of cloud APIs for developers with limited machine learning expertise; chooses the best models & allows for further training of those models for your data (Translation, Vision, Natural Language, Video Intelligence, Tables) cloud.google.com/automl cloud.google.com/automl-tables
- 46. ● General steps a. Prep your training data b. Create dataset c. Import items into dataset d. Create/train model e. Evaluate/validate model f. Make predictions Cloud AutoML: how to use Machine Learning: Cloud ML Engine Google Cloud Machine Learning Engine is a managed service that lets you build, train, and deploy machine learning models (scikit-learn, XGBoost, Keras, TensorFlow), then make predictions with trained models cloud.google.com/ml-engine
- 47. Full Spectrum of AI & ML Offerings App Developer Data Scientist ML Scientist/Researcher Use pre-built models Use/extend OSS SDK ML EngineAuto ML Build custom models ML APIs Other APIs to consider What else may be useful?5
- 48. Big data (move, process, and analyze your data) Storing and Analyzing Data: BigQuery Google BigQuery is a fast, highly scalable, fully-managed data warehouse in the cloud for analytics with built-in machine learning (BQML); issue SQL queries across multi-terabytes of data cloud.google.com/bigquery
- 49. BigQuery: querying Shakespeare words TITLE = "The top 10 most common words in all of Shakespeare's works" QUERY = ''' SELECT LOWER(word) AS word, sum(word_count) AS count FROM [bigquery-public-data:samples.shakespeare] GROUP BY word ORDER BY count DESC LIMIT 10 ''' rsp = BQ.query(body={'query': QUERY}, projectId=PROJ_ID).execute() print('n*** Results for %r:n' % TITLE) for col in rsp['schema']['fields']: # HEADERS print(col['name'].upper(), end='t') print() for row in rsp['rows']: # DATA for col in row['f']: print(col['v'], end='t') print() Top 10 most common Shakespeare words $ python bq_shake.py *** Results for "The most common words in all of Shakespeare's works": WORD COUNT the 29801 and 27529 i 21029 to 20957 of 18514 a 15370 you 14010 my 12936 in 11722 that 11519
- 50. Passing Data & Events: Pub/Sub Google Pub/Sub: a fast, highly scalable, fully-managed multi fan-in/fan-out publisher-subscriber queuing system for messaging & event ingestion (and processing) cloud.google.com/pubsub G Suite APIs Top-level documentation and comprehensive developers overview video at developers.google.com/gsuite
- 51. Other Google APIs & platforms ● Firebase (mobile development platform + RT DB) ○ firebase.google.com ● Google Data Studio (data visualization, dashboards, etc.) ○ marketingplatform.google.com/about/data-studio ● Actions on Google/Assistant/DialogFlow (voice apps) ○ developers.google.com/actions ● YouTube (Data, Analytics, and Livestreaming APIs) ○ developers.google.com/youtube ● Google Maps (Maps, Routes, and Places APIs) ○ developers.google.com/maps ● Flutter (native apps [Android, iOS, web] w/1 code base[!]) ○ flutter.dev 6 Wrap-up Summary & resources for faculty, lecturers, researchers
- 52. ● Key GCP product code samples for students github.com/GoogleCloudPlatform/hackathon-toolkit ● GCP documentation - cloud.google.com/{appengine,functions,vision, language,speech,text-to-speech,translate,automl,firestore,bigquery} ● Like GCP? Wanna use it in class or your research lab? Send your profs to cloud.google.com/edu to apply for teaching or research credits! ● Other docs ○ Firebase - firebase.google.com ○ G Suite - developers.google.com/{gsuite,drive,docs,sheets,slides} ○ Free trial (ignore) and Always Free (FYI) - cloud.google.com/free Resources Learning resources ● Codelabs: self-paced, hands-on tutorials ○ Google codelabs: need a Gmail account, always free ■ g.co/codelabs/cloud ○ Qwiklabs codelabs: don't need a Gmail acct; typically not free ■ google.qwiklabs.com ■ Request free credits ("tokens") at cloud.google.com/edu ● Official GCP documentation ○ cloud.google.com/gcp/getting-started ○ Recommended: Getting Started, Cloud Console, Cloud Shell, Cloud SDK, Community ● YouTube video series: ○ youtube.com/GoogleCloud ○ Recommended: Cloud Minute shorts & Cloud NEXT videos ○ G Suite Dev Show: goo.gl/JpBQ40
- 53. Resources for Higher Education ● Education grant program ○ Teaching grants (per-course basis) ■ $50USD for students & $100USD for faculty & TAs ■ Must exceed "Always Free" daily/monthly quota to incur billing ■ Students will barely use it… average utilization: <25% ■ KEY: not giving Google your personal credit card ○ Research grants ■ Larger amounts; consider as seed funding ■ Over a longer period of time (more than a single term) ○ Apply at cloud.google.com/edu ○ Turnaround time: "within a few business days" ○ Redeem at console.cloud.google.com/edu Thank you! Questions? Wesley Chun @wescpy Progress bars: goo.gl/69EJVw Slides: bit.ly/32kpJbm