AI Services and Serverless Workshop

© 2019, Amazon Web Services, Inc. or its Affiliates.
AI Services and Serverless Workshop
Boaz Ziniman, Technical Evangelist
Amazon Web Services
@ziniman
ziniman
SSID: Guest
Password: Unfabric@@2020

@ziniman
• Overview of Serverless computing and AI Services
• Introduction to AWS services used in the workshop
• Outline of the workshop scenario
• Preview of the labs
What to expect from this session

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Serverless Computing

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In the beginning…

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• Procurement
• Power
• Cooling
• Asset tracking
• Remote hands
• IP transit
• Colocation
• Capacity planning
• Hardware refreshes
• Storage
• Depreciation
• Physical security
• Networking equipment
• Cabling
Physical hardware – concern space

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• Allows developers to obtain compute
capacity on-demand
• Create virtual servers in the cloud with the
click of a button
• Launched in 2006
Amazon Elastic Compute Cloud (EC2)

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Amazon Elastic Compute Cloud (EC2)
Elasticity
Provision Servers in
Minutes
Infrastructure as
Code
Programmatic Networking
Global Footprint
Match Capacity and
Demand

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• Configuration management
• Security patches
• Server utilization
• Operating systems
• Auto-scaling policies
• Monitoring
• Intrusion detection
• Resiliency
• Machine images
• Access management
• Capacity planning
• Hourly billing
• Code deployment
Servers – concern space

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Serverless computing
Fully managed
• No provisioning
• No system administration
• No security patches
• Fault tolerant
Developer productivity
• Focus on your application
• Experiment and innovate quickly
Continuous scaling
• Scale up and down with
demand
• Never pay for idle resources
Build and run applications and services without thinking of servers

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Step Functions

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Application Lifecycle in AWS Step Functions
Visualize in the
Console
Define in JSON Monitor
Executions

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Step Functions Tasks – Service Integrations
AWS Lambda invoke a Lambda function
AWS Batch submit a Batch job and wait for it to complete
Amazon DynamoDB get, put, update or delete an item
Amazon ECS/Fargate run an ECS task and waits for it to complete
Amazon SNS publish a message to a SNS topic
Amazon SQS send a SQS message
AWS Glue start a Glue job
Amazon SageMaker create a training or transform job

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AI Services

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Our mission at AWS
Put machine learning in the hands
of every developer

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M L F R A M E W O R K S &
I N F R A S T R U C T U R E
A I S E R V I C E S
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D
& C O M P R E H E N D
M E D I C A L
L E XR E K O G N I T I O N
V I D E O
Vision Speech Chatbots
A M A Z O N
S A G E M A K E R
B U I L D T R A I N
F O R E C A S TT E X T R A C T P E R S O N A L I Z E
D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization (N E O )
One-click deployment & hosting
M L S E R V I C E S
F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e
E C 2 P 3
& P 3 d n
E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C
I N F E R E N C E
Reinforcement learningAlgorithms & models ( A W S M A R K E T P L A C E
F O R M A C H I N E L E A R N I N G )
Language Forecasting Recommendations
The Amazon ML stack:
Broadest & deepest set of capabilities

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A I S E R V I C E S
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D
& C O M P R E H E N D
M E D I C A L
L E XR E K O G N I T I O N
V I D E O
Vision Speech Chatbots
F O R E C A S TT E X T R A C T P E R S O N A L I Z E
Language Forecasting Recommendations
Put AI to work for your business

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Amazon Rekognition
Easily add intelligent image and video analysis
to your applications.

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Amazon Rekognition:
Deep Learning-Based Image and Video Analysis

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Amazon Rekognition Benefits
Low cost
Your data
is your ownServerless
Rapid
integration
State of the
art capabilities
Continuous
improvement

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Amazon Rekognition Image
Object and scene
detection
Facial
analysis
Face
recognition
Text in
image
Unsafe image
detection
Celebrity
recognition
Face comparison

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Object & Scene Detection

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Smiling?
Facial Analysis

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Crowd Detection – up to 100 faces

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Facial Search

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Explicit Nudity
Nudity
Graphic Male Nudity
Graphic Female Nudity
Sexual Activity
Partial Nudity
Suggestive
Female Swimwear or Underwear
Male Swimwear or Underwear
Revealing Clothes
Image Moderation

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Celebrity Recognition

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Text in Image

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boazz: ~/ aws rekognition detect-labels
--image '{"S3Object":{"Bucket":"demos.ziniman.com","Name":"photos/reko.jpg"}}’
{
"Labels": [
{
"Confidence": 99.14048767089844,
"Name": "Human"
},
{
"Confidence": 99.1404800415039,
"Name": "People"
},
{
"Confidence": 99.14048767089844,
"Name": "Person"
}……
Rekognition API Example

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boazz: ~/ aws rekognition detect-faces
--image '{"S3Object":{"Bucket":"demos.ziniman.com","Name":"photos/reko.jpg"}}'
--attributes "ALL”
{
"FaceDetails": [
{
....
"Gender": {
"Confidence": 99.9211654663086,
"Value": "Male"
},
"AgeRange": {
"High": 52,
"Low": 35
},
....
Rekognition API Example

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Rekognition Lambda Python Example

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The Workshop
Image Recognition and Processing Backend

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What we are going to build?

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What we are going to build?
AWS LambdaAmazon DynamoDB
Amazon S3
AWS Step
Functions
Amazon Rekognition
AWS Lambda
Start state
machine
execution
AWS Lambda
AWS Lambda
AWS Lambda
Extract and
validate image
metadata
from S3 object
(EXIF, size,
format, etc.)
Generate
image
thumbnail
Invoke
Rekognition API
Store data in
DynamoDB

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Step by Step

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• Go to: https://bit.ly/ServerlessAI
• User accounts
• Your own account
• Be careful with production/your company/shared/etc. accounts
• Make sure all services are available in the region you are running in
• WiFi
• We are here to help
• Cleanup
Let the Fun Begin!

Thank You!
Boaz Ziniman, Technical Evangelist
Amazon Web Services
@ziniman
ziniman

@ziniman
https://bit.ly/ServerlessAI
SSID: Guest
Password: Unfabric@@2020

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