1-hr tech talk introducing Machine Learning and the GCP ML APIs and other Google Cloud developer tools to a technical audience.

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
Inspiration
7
Summary &
wrap-up
Caveat: I am NOT a data scientist. I'm a
developer. Like many of you.... So your
journey was also mine. 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
ai.googleblog.com/2016/09/a-neural-network-for-machine.html
Google Photos
The app has a
search bar?!?
blog.google/products/photos/smarter-photo-albums-without-work

7. # 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
rules
data
Traditional
Programming
answers
answers
data
rules
Machine
Learning

9. Inference Phase
Training Phase
answers
data
model ("rules")
Machine
Learning
Model
predictions
(new) data
2 Introduction to
Google Cloud
GCP and Google Workspace
(formerly G Suite) tools & APIs

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

11. How can Google Cloud help (industry)?
● What can we provide developers?
○ 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 you already use (Google Workspace
(formerly G Suite))
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

12. Full Spectrum of AI & ML Offerings
App developer Data scientist,
developer
Data scientist, Researcher
(w/infrastructure access &
DevOps/SysAdmin skills)
AI Platform
(now Vertex AI)
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?

14. 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

15. ● 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

16. 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!

17. 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

18. $ 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

19. Simple sentiment & classification analysis
from google.cloud import language
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 = language.LanguageServiceClient()
document = language.types.Document(content=TEXT,
type=language.enums.Document.Type.PLAIN_TEXT)
print('TEXT:', TEXT) # sentiment analysis
sent = NL.analyze_sentiment(document).document_sentiment
print('nSENTIMENT: score (%.2f), magnitude (%.2f)' % (sent.score, sent.magnitude))
print('nCATEGORIES:') # content classification
categories = NL.classify_text(document).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)

20. 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
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

21. 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

22. Machine Learning: Vertex AI
Cloud Vertex AI (formerly AI platform) is a
managed service providing: 1) APIs backed by
pre-trained models, 2) ability to further
train those models, 3) ability to build, train
& deploy ML models (scikit-learn, XGBoost,
Keras, TensorFlow), then make predictions with
trained models, all in a unified platform
cloud.google.com/vertex-ai
Other Cloud tools
These may also be helpful
5

23. Other Cloud APIs/services
"Friends of AI/ML" companion services
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

24. 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']))
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

25. ● BigQuery public data sets: cloud.google.com/bigquery/public-data
● BQ sandbox (1TB/mo free): cloud.google.com/bigquery/docs/sandbox (see blog post)
● Other public data sets: cloud.google.com/public-datasets (Google Cloud),
research.google/tools/datasets (Google Research), and Kaggle (kaggle.com)
● COVID-19 BigQuery data sets
○ 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 and public data sets
Storing Data: Cloud Storage, Filestore, Persistent Disk
cloud.google.com/storage
cloud.google.com/filestore
cloud.google.com/persistent-disk

26. 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 Firestore Datastore & Firestore Native modes:
see cloud.google.com/datastore/docs/firestore-or-datastore)
cloud.google.com/firestore

27. 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
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

28. 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)
Serverless compute platforms
Google App Engine, Cloud Functions, Cloud Run; Apps Script

29. cloud.google.com/hosting-options#hosting-options
Google Cloud compute option spectrum
Compute
Engine
Kubernetes
Engine (GKE)
Cloud Run
on Anthos
Cloud Run
(fully-mgd)
App Engine
(Flexible)
App Engine
(Standard)
Cloud
Functions
> 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*?

30. Serverless: what & why
● What is serverless?
○ Misnomer (a "PMM") :-)
○ "No worries"
○ Developers focus on writing code & solving business problems*
○ Servers (physical & virtual) completely abstracted away from the user*
● Why serverless?
○ Fastest growing segment of cloud... per analyst research:
■ $1.9B (2016) and $4.25B (2018) ⇒ $7.7B (2021), $14.93B (2023), and $21.1B (2025)^
■ $4.18B (2018) and $6.05B (2020) ⇒ $31.53B (2026) and $53.08B (2028)†
○ What if you go viral? Autoscaling: your new best friend
○ What if you don't? Code not running? You're not paying.
* Forbes (May 2018)
^ (in USD) CB Insights (Sep 2018), MarketsandMarkets™ (Jan 2019)
† (in USD) Reports and Data (Jul 2019 , Jan 2020, and Oct 2021)
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

31. 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 short utilities (alerts, ETL...), monoliths →
loosely-coupled microservices, event-driven tasks
● 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

32. main.py
def hello_world(request):
return 'Hello World!'
Deploy:
$ gcloud functions deploy hello --runtime python39 --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
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

33. The rise of containers... ● Any language
● Any library
● Any binary
● Ecosystem of base images
● Industry standard
FLEXIBILITY
“We can’t be locked in.”
“How can we use
existing binaries?”
“Why do I have to choose between
containers and serverless?”
“Can you support language _______ ?”
Serverless inaccessible for some...
CONVENIENCE

34. Cloud Run: code, build, deploy
.js .rb .go
.sh
.py ...
● Any language, library, binary
○ HTTP port, stateless
● Bundle into container
○ Build with Docker OR Cloud Build
○ Image ⇒ Container/Artifact Registry
● Deploy to Cloud Run (managed or GKE)
● GitOps: CD push-to-deploy from Git
○ See documentation & announcement
○ CI/CD with GitHub Actions tutorial
● Cloud Buildpacks: Docker, Dockerfiles,
and knowledge of containers optional
State
HTTP
Hello World (Python "MVP")
main.py
import os
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello_world():
return 'Hello World!'
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))
cloud.google.com/run/docs/quickstarts/build-and-deploy
requirements.txt
flask

35. ● Build containers easily & securely without creating/managing Dockerfiles
● Open source, open standard; based on CNCF Buildpacks spec v3
● Used by GCF Functions Framework to deploy locally-developed functions
● Supports most common development tools
○ Go 1.10+
○ Node.js 10+
○ Python 3.7+
○ Java 8 & 11
○ .NET Core 3.1+
● Blog posts
○ See cloud.google.com/blog/products/containers-kubernetes/google-cloud-now-supports-buildpacks and
cloud.google.com/blog/products/serverless/build-and-deploy-an-app-to-cloud-run-with-a-single-command
Deploy to Cloud Run with Buildpacks
github.com/GoogleCloudPlatform/buildpacks
$ ls
index.js package.json
$ gcloud run deploy myapp --source .
$ ls
main.py requirements.txt
$ gcloud run deploy myapp --source .
Node.js, Go,
Python, etc.?
It's the same
command!
Serverless common use cases App Engine Cloud Run
Cloud
Functions
Web services
Web app hosting/custom domains ✓ ✓
HTTP services ✓ ✓ ✓
Container hosting ✓
APIs
Web & mobile backends ✓ ✓
Internal/intranet apps ✓ ✓
Large-scale data processing ✓ ✓
Automation
Workflow & orchestration ✓ ✓
Event-driven automation ✓ ✓
GitOps: Git push-to-deploy (CD-only) ✓
Common use cases

36. Flexibility in options
Cloud
Functions
App
Engine
Cloud
Run
local
server
Cloud
Translation
My "Google Translate" MVP
goo.gle/2Y0ph5q
● "Nebulous" sample web app
○ Flask/Python 2 or 3
○ Express/Node.js 10+
○ Uses Cloud Translation API
● Deployable to on-prem server
● Also GCP serverless compute
○ App Engine
○ Cloud Functions
○ Cloud Run
● With only config changes
● No changes to app code
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

37. “Hello World!” in Apps Script
Sheets-bound “Hello World!”
Apps Script intro
goo.gl/1sXeuD

38. What can you do with this?
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

39. 6 Inspiration
Mix-and-match Google APIs and
serverles to create unique solutions

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

41. 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

42. ● 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)

43. Store big data results

44. Visualize big data results
Ingest data from Sheets

45. Link to chart in Sheets

46. 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

47. 7 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

48. 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 and Jupyter Notebooks
Users have many ways to access an indispensable data science tool
● Google Cloud Vertex AI Workbench
○ cloud.google.com/notebooks
● Kaggle
○ kaggle.com
● Google Research CoLaboratory
○ colab.research.google.com
● Google Cloud Dataproc Hub
○ cloud.google.com/dataproc/docs/tutorials/dataproc-hub-overview
● Google Cloud Datalab
○ cloud.google.com/datalab/docs/how-to/working-with-notebooks

49. quickdraw.withgoogle.com
Cloud Vision demo: Quick Draw game
experiments.withgoogle.com/quick-draw
vision-explorer.reactive.ai
Another Cloud Vision demo: Vision Explorer
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

50. Other Google APIs & platforms
● Google Workspace (G Suite) (code Gmail, Drive, Docs, Sheets, Slides!)
○ developers.google.com/gsuite
● Firebase (mobile development platform and RT DB plus ML-Kit)
○ firebase.google.com and 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