1-1.5-hr tech talk introducing Machine Learning and the GCP ML APIs and other Google Cloud developer tools.

1. Google Cloud developer tools + an
Easyier path to machine learning
Wesley Chun - @wescpy
Developer Advocate, Google
Adjunct CS Faculty, Foothill College
Developer Advocate, Google Cloud
● Mission: enable current and future
developers everywhere to be
successful using Google Cloud and
other Google developer tools & APIs
● Focus: GCP serverless (App Engine,
Cloud Functions, Cloud Run); higher
education, Google Workspace, GCP
AI/ML APIs; multi-product use cases
● Content: speak to developers globally;
make videos, create code samples,
produce codelabs (free, self-paced,
hands-on tutorials), publish blog posts
About the speaker
Previous experience / background
● Software engineer & architect for 20+ years
○ Yahoo!, Sun, HP, Cisco, EMC, Xilinx
○ Original Yahoo!Mail engineer/SWE
● Technical trainer, teacher, instructor
○ Taught Math, Linux, Python since 1983
○ Private corporate trainer
○ Adjunct CS Faculty at local SV college
● Python community member
○ Popular Core Python series author
○ Python Software Foundation Fellow
● 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
● Big data is everywhere now; need power of AI/ML to analyze
● Requires certain level of math/statistics, gives AI/ML learning curve
● APIs powered by ML helps ease this burden
● If you can call APIs, you can use ML!
● Google provides AI/ML and many other tools to he
1
What is ML?
2
Introducing
Google Cloud
3
Google APIs
4
Cloud
ML APIs
5
Other Cloud tools
& APIs
6
Serverless
platforms
7
Inspiration
8
Summary &
wrap-up
Caveat: I am NOT a data scientist.
(I studied NW/distributed systems in
school.) Like many of you, I've seen the
rise of big data and gotten caught up in
the excitement of ML, so this is also part
of my journey. However...
I can call APIs, therefore I am.

3. What is machine learning?
AI, ML, and making computers smarter; to help us
understand more and get more insights than before
1

4. AI & Machine Learning
Puppy or muffin?
Source:
twistedsifter.com/2016/03/puppy-or-bagel-meme-gallery
Machine learning is learning
from rules plus experience.

5. Deep Neural Networks Step 1: Training
Deep Neural Networks Step 2: Testing

6. Google Translate
Google Photos
Did you ever stop
to notice this app
has a search bar?!?

7. 2012 2013 2014 2015 2016
0
1000
2000
3000
4000
# of directories containing neural net model description files
Use of Deep Learning at Google accelerated rapidly
Global view
Problem
● 1B ppl depend on seafood
● 85% at/over-fishing or recovering
● 20% caught illegal, undoc'd, unreg'd
● Analysts monitoring unscalable
One solution
● globalfishingwatch.org/map
● Machine-learning classifiers:
○ Ship type: cargo, tug, sail, fishing
○ Ship size
○ Gear: longline, purse seine, trawl
○ Movement tracking: when and
where vessels are fishing

8. blog.google/products/google-cloud/how-ai-can
-help-make-safer-baby-food-and-other-products
youtu.be/gC1b6fBo7aE
Two tales of food sorting
with the help of AI
opensource.com/article/17/9/tensorflow
youtu.be/4HCE1P-m1l8
Lots of data
Complex mathematics in
multidimensional spaces
Magical results
Popular imagination of what Machine Learning is

9. Organize data
Use machines to
flesh out the
model from data
Collect
data
Create model
Deploy fleshed
out model
In reality what ML is
Large Datasets Good Models Lots Of Computation
Keys to Successful Machine Learning

10. rules
data
Traditional
Programming
answers
answers
data
rules
Machine
Learning
Inference Phase
Training Phase
answers
data
model ("rules")
Machine
Learning
Model
predictions
(new) data

11. Fashion MNIST
● 10 categories
● Images: 28x28 pixels
● 70k grayscale images
● Go train a neural net!
tensorflow.org/tutorials/
keras/classification
import tensorflow as tf
from tensorflow import keras
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
09 09 = ankle boot;
踝靴;
アンクルブーツ;
Bróg rúitín

12. Your steps
1. Import MNIST dataset
2. Explore/preprocess data
3. Build model
a. Setup layers
b. Compile model
4. Train model
5. Evaluate accuracy
6. Make predictions
7. (Have fun!)
2 Introduction to
Google Cloud
GCP and Google Workspace
(formerly G Suite) tools & APIs

13. YOUR
NEXT
APP? google.com/datacenter
formerly
( )

14. How can Google Cloud help (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 (& professionals) already use
○ Education grants (use our cloud w/o personal credit cards)
ML "building block" 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!
● cloud.google.com/products/ai/building-blocks
Vision Video
Intelligence
Speech
(S2T & T2S)
Natural
Language
Translation

15. Full Spectrum of AI & ML Offerings
App developer Data scientist,
developer
Data scientist, Researcher
(w/infrastructure access &
DevOps/SysAdmin skills)
AI Platform
Auto ML
Build custom models,
use OSS SDK on fully-
managed infrastructure
ML APIs
App developer,
data scientist
Use/customize pre-built
models
Use pre-built/pre-
trained models
Build custom models, use/
extend OSS SDK, self-manage
training infrastructure
3 Google APIs
What are they? How do you use them?

17. General steps
1. Go to Cloud Console
2. Login to Google/Gmail account
(Workspace domain may require admin approval)
3. Create project (per application)
4. Enable APIs to use
5. Enable billing (CC, Free Trial, etc.)
6. Download client library(ies)
7. Create & download credentials
8. Write code
9. Run code (may need to authorize)
Google APIs: how to use
Costs and pricing
● GCP: pay-per-use
● Google Workspace: subscription
● GCP Free Trial ($300/1Q, CC req'd)
● GCP "Always Free" tier
○ Most products have free tier
○ Daily or monthly quota
○ Must exceed to incur billing
● More on both programs at
cloud.google.com/free
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

18. ● 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
&
Google APIs client
libraries for many
languages; demos in
developers.google.com/api-
client-library
cloud.google.com/apis/docs
/cloud-client-libraries

19. 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)
4 Cloud ML APIs
Easier path to ML by simply calling APIs!

20. Machine Learning: Cloud Vision
Google Cloud Vision API lets developers
extract metadata and understand the
content of an image, identify & detect
objects/labels, text/OCR, landmarks,
logos, facial features, products, XC, etc.
cloud.google.com/vision
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(label.description, '(%.2f%%)' % (label.score*100.))
Vision: label annotation/object detection

21. $ 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
g.co/codelabs/vision-python
Machine Learning: Cloud Natural Language
Google Cloud Natural Language API
reveals the structure and meaning
of text, performing sentiment
analysis, content classification,
entity extraction, and syntactical
structure analysis; multi-lingual
cloud.google.com/language

22. 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.'''
NL = discovery.build('language', 'v1', developerKey=API_KEY)
print('TEXT:', TEXT) # sentiment analysis
data = {'type': 'PLAIN_TEXT', 'content': TEXT}
sent = NL.documents().analyzeSentiment(
body={'document': data}).execute().get('documentSentiment')
print('nSENTIMENT: score (%.2f), magnitude (%.2f)' % (
sent['score'], sent['magnitude']))
print('nCATEGORIES:') # content classification
categories = NL.documents().classifyText(
body={'document': data}).execute().get('categories')
for cat in categories:
print('* %s (%.2f)' % (cat['name'][1:], cat['confidence']))
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 (0.20), magnitude (0.50)
CATEGORIES:
* Internet & Telecom (0.76)
* Computers & Electronics (0.64)
* News (0.56)

23. 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
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']))

24. 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

25. 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
Machine Learning: AutoML
AutoML: a suite of cloud APIs for
developers with limited machine
learning expertise; auto-selects 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

26. ● 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 AI Platform
Google Cloud AI Platform 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/ai-platform

27. 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
BigQuery: querying Shakespeare words
TITLE = "The 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.jobs().query(body={'query': QUERY}, projectId=PROJ_ID).execute()
print('n*** Results for %r:n' % TITLE)
print('t'.join(col['name'].upper() # HEADERS
for col in rsp['schema']['fields']))
print('n'.join('t'.join(str(col['v']) # DATA
for col in row['f']) for row in rsp['rows']))

28. 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
● BigQuery public data sets: cloud.google.com/bigquery/public-data
● BQ sandbox (1TB/mo free): cloud.google.com/bigquery/docs/sandbox
● Other public data sets: cloud.google.com/public-datasets (Google Cloud),
research.google/tools/datasets (Google Research), and Kaggle (kaggle.com)
● COVID-19
○ How to use our data sets (see blog post)
○ JHU Coronavirus COVID-19 Global Cases data set
○ List of all COVID-19 data sets
● Cloud Life Sciences API: cloud.google.com/life-sciences (see blog post)
● Cloud Healthcare API: cloud.google.com/healthcare (see blog post)
BigQuery & public data sets
Spring 2020

29. Other Cloud tools
These may also be helpful
5
Storing Data: Cloud Storage, Filestore, Persistent Disk
cloud.google.com/storage
cloud.google.com/filestore
cloud.google.com/persistent-disk

30. 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
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
(For choosing between Firebase & Cloud Firestore: see
firebase.google.com/docs/firestore/rtdb-vs-firestore;
for choosing between Cloud Datastore & Firestore: see
cloud.google.com/datastore/docs/firestore-or-datastore)
cloud.google.com/firestore

31. Google Workspace: Google Sheets
Sheets API gives you programmatic
access to spreadsheets; perform
(w/code) almost any action you can
do from the web interface as a user
developers.google.com/sheets
Try our Node.js customized reporting tool codelab:
g.co/codelabs/sheets
Why use the Sheets API?
data visualization
customized reports
Sheets as a data source

32. Migrate SQL data to a Sheet
# read SQL data then create new spreadsheet & add rows into it
FIELDS = ('ID', 'Customer Name', 'Product Code',
'Units Ordered', 'Unit Price', 'Status')
cxn = sqlite3.connect('db.sqlite')
cur = cxn.cursor()
rows = cur.execute('SELECT * FROM orders').fetchall()
cxn.close()
rows.insert(0, FIELDS)
DATA = {'properties': {'title': 'Customer orders'}}
SHEET_ID = SHEETS.spreadsheets().create(body=DATA,
fields='spreadsheetId').execute().get('spreadsheetId')
SHEETS.spreadsheets().values().update(spreadsheetId=SHEET_ID, range='A1',
body={'values': rows}, valueInputOption='RAW').execute()
Migrate SQL data
to Sheets
goo.gl/N1RPwC
Google Workspace: Google Drive
Drive API allows developers to read,
write, control permissions/sharing,
import/export files, and more!
developers.google.com/drive

33. Google Workspace: Google Docs & Slides
Docs & Slides APIs give you access
to read or write documents and
presentations programmatically so
you can auto-generate them with data
integrated from various sources
developers.google.com/docs
developers.google.com/slides
Running Code: Compute Engine
>
Google Compute Engine delivers
configurable virtual machines
of all shapes and sizes, from
"micro" to 416 vCPUs, 11.776
TB RAM, 256 TB HDD or SSD
disk; GPUs & TPUs
(Debian, CentOS, CoreOS, SUSE, Red Hat Enterprise
Linux, Ubuntu, FreeBSD; Windows Server 2008 R2, 2012
R2, 2016, 1803, 1809, 1903/2019, 1909)
cloud.google.com/compute

34. Machine Learning: Cloud TPUs
The Google Cloud TPU API lets
developers pair Compute Engine VMs
along with Tensor Processing Units
(TPUs) to train machine learning models
faster and at a lower cost than GPUs*.
cloud.google.com/tpu
* source: Google Cloud blog (Dec 2018)
6 Serverless
platforms
Where to run your code

35. > Google Compute Engine configurable
VMs of all shapes & sizes, from
"micro" to 416 vCPUs, 11.776 TB
RAM, 256 TB HDD/SSD plus Google
Cloud Storage for data lake "blobs"
(Debian, CentOS, CoreOS, SUSE, Red Hat Enterprise Linux,
Ubuntu, FreeBSD; Windows Server 2008 R2, 2012 R2, 2016, 1803,
1809, 1903/2019, 1909)
cloud.google.com/compute
cloud.google.com/storage
Yeah, we got VMs & big disk… but why*?
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)

36. Running Code: App Engine
Got a great app idea? Now what?
VMs? Operating systems? Big disk?
Web servers? Load balancing?
Database servers? Autoscaling?
With Google App Engine, you don't
think about those. Just upload
your code; we do everything else.
>
cloud.google.com/appengine
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
● Test w/local dev server

37. Hello World (Python "MVP")
app.yaml
runtime: python38
main.py
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello():
return 'Hello World!'
requirements.txt
Flask>=1.1.2
Deploy:
$ gcloud app deploy
Access globally:
PROJECT_ID.appspot.com
cloud.google.com/appengine/docs/standard/python3/quickstart
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

38. 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 (in-browser)
○ Develop/test locally with Functions Framework
● Cloud Functions for Firebase
○ Mobile app use-cases
● Available runtimes
○ JS/Node.js 8, 10, 12, 14
○ Python 3.7, 3.8, 3.9
○ Go 1.11, 1.13
○ Java 11
○ Ruby 2.6, 2.7
○ .NET Core 3.1
main.py
def hello_world(request):
return 'Hello World!'
Deploy:
$ gcloud functions deploy hello --runtime python38 --trigger-http
Access globally (curl):
$ curl REGION-PROJECT_ID.cloudfunctions.net/hello
Access globally (browser):
https://REGION-PROJECT_ID.cloudfunctions.net/hello
Hello World (Python "MVP")
cloud.google.com/functions/docs/quickstart-python

39. 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!
Need custom HW? Cloud Run on GKE
cloud.google.com/run
Google Workspace: Apps Script
Apps Script: A customized serverless JS
runtime for automation, and extension
and integration with Google Workspace
(formerly G Suite), Google, or other
external services. Access 40+ different
Google services w/o using APIs or
writing OAuth code.
developers.google.com/apps-script

40. +JavaScript
-API “flavor”
+built-in
“flavor”
-OAuth2 (you)
!=
“Hello World!” in Apps Script

41. Sheets-bound “Hello World!”
Apps Script intro
goo.gl/1sXeuD
What can you do with this?

42. Accessing maps from
spreadsheets?!?
goo.gl/oAzBN9
This… with help from Google Maps & Gmail
function sendMap() {
var sheet = SpreadsheetApp.getActiveSheet();
var address = sheet.getRange("A2").getValue();
var map = Maps.newStaticMap().addMarker(address);
GmailApp.sendEmail('friend@example.com', 'Map',
'See below.', {attachments:[map]});
}
JS
g.co/codelabs/apps-script-intro

43. ● Extend functionality of Google
Workspace (formerly G Suite) editors
● Embed your app within ours!
● 2014: Google Docs, Sheets, Forms
● 2017 Q3: Google Slides
● 2017 Q4: Gmail
● 2018 Q1: Google Chat bots
● Apps Script also powers Google Data
Studio community connectors, and
Google Ads scripts
Apps Script powers add-ons… and more!
7 Inspiration
Use Google APIs to create unique solutions

44. Custom intelligence in Gmail
Analyze Google Workspace (formerly G Suite) data with GCP

45. Gmail message processing with GCP
Gmail
Cloud
Pub/Sub
Cloud
Functions
Cloud
Vision
Workspace
(formerly G Suite)
GCP
Star
message
Message
notification
Trigger
function
Extract
images
Categorize
images
Inbox augmented with Cloud Function

46. ● Gmail API: sets up notification forwarding to Cloud Pub/Sub
● developers.google.com/gmail/api/guides/push
● Pub/Sub: triggers logic hosted by Cloud Functions
● cloud.google.com/functions/docs/calling/pubsub
● Cloud Functions: "orchestrator" accessing GCP (and Google Workspace/G Suite) APIs
● Combine all of the above to add custom intelligence to Gmail
● Deep dive code blog post
● cloud.google.com/blog/products/application-development/
adding-custom-intelligence-to-gmail-with-serverless-on-gcp
● Application source code
● github.com/GoogleCloudPlatform/cloud-functions-gmail-nodejs
App summary
Big data analysis to slide presentation
Access GCP tools from Google Workspace (formerly G Suite)

47. Store big data results

48. Visualize big data results
Ingest data from Sheets

49. Link to chart in Sheets

50. Supercharge Workspace (G Suite) with GCP
Workspace (G Suite) GCP
BigQuery
Apps Script
Slides Sheets
Application
request
Big data
analytics
App summary
● Leverage GCP and build the "final mile" with Google Workspace (formerly G Suite)
● Driven by Google Apps Script
● Google BigQuery for data analysis
● Google Sheets for visualization
● Google Slides for presentable results
● "Glued" together w/Google Workspace (formerly G Suite) serverless
● Build this app (codelab): g.co/codelabs/bigquery-sheets-slides
● Video and blog post: bit.ly/2OcptaG
● Application source code: github.com/googlecodelabs/bigquery-sheets-slides
● Presented at Google Cloud NEXT (Jul 2018 [DEV229] & Apr 2019 [DEV212])
● cloud.withgoogle.com/next18/sf/sessions/session/156878
● cloud.withgoogle.com/next/sf/sessions?session=DEV212

51. 8 Wrap-up
Summary and resources
Machine learning session summary
● What is machine learning again?
○ Solving harder problems by making computers smarter
○ "Using data to answer questions.” ~Yufeng Guo, Google Cloud
● How do you do machine learning again?
○ Collect lots of data
○ Build and train your model then validate it
○ Use your model to make predictions on new data
● Do you need lots of machine learning experience to get started?
○ No: use pre-trained models available via APIs
○ No: need to do training? Consider using AutoML APIs
○ Build your experience then use standard OSS library when ready

52. Google APIs and ML resources
● Google Workspace (G Suite), Google Apps Script docs, repo, videos
○ developers.google.com/gsuite
○ github.com/googleworkspace
○ goo.gl/JpBQ40
● Google Cloud Platform (GCP) documentation & open source repos
○ cloud.google.com/products/ai/building-blocks
○ github.com/GoogleCloudPlatform
○ youtube.com/GoogleCloudPlatform
● Your next steps…
○ Customize our ML APIs with AutoML: cloud.google.com/automl
○ Get hands-on with a Cloud ML codelab (self-paced tutorial): gcplab.me
○ Explore: Kaggle (kaggle.com) and Colab (colab.research.google.com)
● Google Cloud Platform (GCP)
○ Code samples for students: goo.gle/hackathon-toolkit
○ Videos: youtube.com/GoogleCloudPlatform
○ Google Codelabs (free, self-paced, hands-on tutorials): gcplab.me
○ QwikLabs Codelabs (not free but don't need Google acct): google.qwiklabs.com
● GCP documentation - cloud.google.com/{docs,appengine,functions,run,vision,automl,translate,
language, speech,texttospeech,video-intelligence,firestore,bigquery,compute,storage,gpu,tpu}
● Like GCP? Wanna use it in class or your research lab? Send your profs to cloud.google.com/edu
to apply for GCP teaching/research credits! Students can apply for QwikLabs/training credits!
● Know AWS/Azure? Compare w/GCP at cloud.google.com/docs/compare/{aws,azure}
● Others
○ GCP Free Trial (ignore) and Always Free (tier) - cloud.google.com/free
○ Mobile apps: Firebase - firebase.google.com and Flutter - flutter.dev
○ Workspace - developers.google.com/{gsuite,drive,docs,sheets,slides} and goo.gl/JpBQ40
○ Other Google (non-Cloud) codelabs: g.co/codelabs
○ Developer Student Clubs - g.co/dev/dsc - Global Google student developer groups
○ Internships & other career stuff - google.com/students
Resources (students)

53. Higher 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"
quickdraw.withgoogle.com
Google Cloud Vision demo "experiment"
experiments.withgoogle.com/quick-draw

54. vision-explorer.reactive.ai
Vision Explorer: NEXT '16: Cloud Vision demo
cloud.google.com/blog/products/gcp/explore-the-galaxy-of-images-with-cloud-
vision-api and open-source repo at github.com/cogentlabs/cloud-vision-explorer
FYI and FYA (if you/your students love comics)
cloud.google.com/products/ai/ml-comic-[12]
... ...

55. Other Google APIs & platforms
● Firebase (mobile development platform + RT DB; ML Kit)
○ firebase.google.com & firebase.google.com/docs/ml-kit
● Google Data Studio (data visualization, dashboards, etc.)
○ datastudio.google.com/overview
○ goo.gle/datastudio-course
● 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
Thank you!
Wesley Chun
@wescpy
Video: youtu.be/ja4E9Dzr0Gw
Progress bars: goo.gl/69EJVw