Women in Big Data

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
WiBD Meetup AWS Loft
Wednesday, November 15th 2017

Agenda
3:00pm-3:10pm Welcome
3:10pm-3:30pm Trends and Opportunities in the Big Data Space
3:30pm-4:15pm Big Data Overview
4:15pm-4:45pm Artificial Intelligence and Machine Learning
4:45pm-6:00pm Q&A and Networking Happy Hour
Gunjan Sharma
Big Data Platform Lead, Capital
Janisha Anand
Big Data Consultant, AWS
Shree Kenghe
Solutions Architect, AWS
SPEAKERS

Did You Know..
Demand for computing jobs will
increase by half a million in the next
decade.
Companies are struggling to find
qualified employees - women
provide untapped potential
Women prefer to enter professions
where they can have a beneficial
impact on people and society
Computers must be taught the very best of what humanity has to offer, not just one
slice of the human experience – Melinda Gates
Women In Tech
Technology jobs in US
Facebook
Twitter
25%
15%
10%
Leadership positions in
technology
Apache chairs/leads
<5%
<3%
Big Data users and decision
makers
<7%

Not just a Leaky Pipeline

WiBD Mission and History
Inspire, connect, grow, and champion the success of women in Big Data
 Started at Intel with 15 members in
June’15
 Core leadership team with
representation from Intel, SAP and
Cloudera
 Grown to ~3000 members representing
60 companies & universities
 Organized by regions and chapters
Inspire: influence with key partners across
our community.
Connect: embrace and cultivate a community
of diverse women in big data and analytics.
Grow: build, educate, support our women of
today for tomorrow’s challenges
Champion: elevate women and celebrate
their successes

Women in Big Data Committees

WiBD Presence
Region Core Members
Administration
Shala Arshi, Intel
Ziya Ma, Intel
Tina Tang, SAP
Deborah Wiltshire, Cloudera
Karmen Leung, IBM
US East
Chapter Director: Gunjan Sharma, Capital One, gsharma447@gmail.com
NYC Satellite: Debika Sharma, MapR, dsharma@mapr.com
US West
Chapter Director: Radhika Rangarajan, Intel
radhika.rangarajan@intel.com
US Mid-West
Chapter Director: Maleeha Qazi, American Family Insurance
MQAZI@amfam.com
Europe
Chapter Director: Tina Rosario, SAP
Tina.rosario@sap.com

Our Website
www.womeninbigdata.org/

Our Sponsors / Partners

Membership, Partnership & Sponsorship
Why Join
• Attend Meetups.
• Join industry trainings by experts.
• Network with colleagues in Big Data.
How to Join
• LinkedIn: www.linkedin.com/groups/6981086
• Find your local meetup here: https://www.womeninbigdata.org/wibd-structure/ or join the NYC
meetup at https://www.meetup.com/Women-in-Big-Data-NYC/
• Also reference: womeninbigdata.org and follow us @DataWomen
Get More Involved
• Help set our direction—sign up for a committee (Administration, Awareness & Evangelism,
Networking, Training and Mentorship).
• Host a meetup or training in your local area.
• Contribute blog posts on womeninbigdata.org and social media.
• Be a contributing member of the discussion: www.linkedin.com/groups/6981086

Big Data Trends and Outlook

Big Data Evolution
Data Generation
- social media,
websites, real-
time streaming
capability
Data Capture
into filesystems,
lakes, NoSQL
Databases
Data cleansing
and
transformations
using heavy
duty ETL tools
such as Spark
and MapReduce
Insight
generation via
simple data
analysis and
visualization
tools to add
business value
Sophisticated
insights via
pattern
recognition and
predictive
analytics
Advances in
Artificial
Intelligence
enabling real-
time actions and
simulation of
human behavior

AI to become production-ready in enterprises to improve sales and marketing, cut costs via
automation of low skilled tasks, improve customer service.
Data science as a commodity
• Higher level frameworks and libraries to make modeling easier
• AI as a service – Ripe for innovations that reduce barriers to entry by creating platforms
that can be used by a wider audience
Cloud will be the underpinning of all data science and machine learning activity
Roles and skill-sets will continue to evolve
• Data scientist shortage will lead to re-training of existing workforce on commodity platforms
• Overlap and consolidation of roles – data scientist, modeler, ML engineer
Data and Analytics Trends

Data Scientist- does the statistical analysis/research to determine which machine leaning approach
to use, models the algorithm, and prototypes it usually in R, Python / PySpark.
Machine Learning Engineer- partners with the Data Scientist to take the ML model prototyped by
the Data Scientist and make it work well in a production environment at scale, by coding it in a more
robust language like Scala, JAVA or C++ and utilizing faster data piping and parallel processing
(Spark, MapReduce, etc.)
Data Engineer– designs, constructs and maintains databases and large-scale distributed processing
systems
Other roles– Product Manager, Solution Architect
Roles in the Data Ecosystem

How to Join
• LinkedIn: www.linkedin.com/groups/6981086
• Find your local meetup here: https://www.womeninbigdata.org/wibd-structure/ or join the NYC
meetup at https://www.meetup.com/Women-in-Big-Data-NYC/
• Also reference: womeninbigdata.org and follow us @DataWomen
Get More Involved
• Help set our direction—sign up for a committee (Administration, Awareness & Evangelism,
Networking, Training and Mentorship).
• Host a meetup or training in your local area.
• Contribute blog posts on womeninbigdata.org and social media.
• Be a contributing member of the discussion: www.linkedin.com/groups/6981086

Big Data at AWS Overview

What to Expect from this
Session
 Current Trends
 Design Patterns
 Core Components for Big Data Workloads
 Building a Big Data Application on AWS
 Q & A

Big Data is Breaking Traditional IT
Infrastructure
Volume
Velocity
Variety
- Too many tools to setup, manage & scale
- Unsustainable costs
- Difficult & undifferentiated heavy lifting just to
get started
- New & expensive skills
- Bigger responsibility (sensitive data)

A Flywheel For Data
Artificial Intelligence
More Users Better Products
More Data Better Analytics
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
Athena
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data

Machine Learning
Deep Learning
AI
Big Data
More Users Better Products
A Flywheel For Data

The Evolution of Big Data Processing
Descriptive Real-time Predictive
Dashboards;
Traditional query &
reporting
Clickstream analysis;
Ad bidding;
streaming data
Inventory forecasting;
Fraud detection;
Recommendation
engines
What happened
before
What’s happening
now
Probability of “x”
happening

AWS Big Data Portfolio
Collect Store Analyze
AWS Direct
Connect
AWS
Snowball / Snowmobile
Amazon Kinesis
Amazon S3 Amazon Glacier
Amazon
DynamoDB
Amazon
Elasticsearch Service
Amazon EMR
Amazon EC2
Amazon
Redshift
Amazon Machine
Learning
Amazon
QuickSight
AWS Data PipelineAWS Database Migration Service
Amazon Athena
AWS Glue
(Coming Soon)
AWS IoT
AWS
Lambda

Simplify Big Data Processing
Time to Answer (Latency)
Throughput
Collect /
Ingest
Store Process /
Analyze
Consume /
Visualize
Data Answers
&
Insights
Time to Answer (Latency)
Throughput
Cost
START HERE
WITH A BUSINESS CASE

Architectural Principles
Build decoupled systems
• Data → Store → Process → Store → Analyze → Answers
Use the right tool for the job
• Data structure, latency, throughput, access patterns
Leverage AWS managed services
• Scalable/elastic, available, reliable, secure, no/low admin
Use log-centric design patterns
• Immutable logs, materialized views
Big data ≠ big cost

Multi-Stage Decoupled “Data Bus”
Multiple stages
Storage decouples multiple processing stages
Store Process Store ProcessData
process
store

Storage Compute
• Decoupled “data bus”
• Completely autonomous components
• Independently scale storage & compute resources on-demand
Collect → Store → Process | Analyze → Consume
Multi-Stage Decoupled “Data Bus”

What Is the Temperature of Your Data ?

Hot Warm Cold
Volume MB–GB GB–TB PB–EB
Item size B–KB KB–MB KB–TB
Latency ms ms, sec min, hrs
Durability Low–high High Very high
Request rate Very high High Low
Cost/GB $$-$ $-¢¢ ¢
Hot data Warm data Cold data
Data Characteristics: Hot, Warm, Cold

Spark Streaming
Apache Storm
AWS Lambda
KCL apps
Amazon
Redshift
Amazon
Redshift
Hive
Spark
Presto
Amazon Kinesis Amazon
DynamoDB
Amazon S3data
Hot Cold
Data temperature
Processingspeed
Fast
Slow Answers
Hive
Native apps
KCL apps
AWS Lambda
Amazon
Athena

Streaming
Amazon Kinesis
Analytics
KCL
apps
AWS Lambda
COLLECT STORE CONSUMEPROCESS / ANALYZE
Amazon Elasticsearch
Service
Apache Kafka
Amazon SQS
Amazon Kinesis
Streams
Amazon Kinesis
Firehose
Amazon DynamoDB
Amazon S3
Amazon ElastiCache
Amazon RDS
Amazon DynamoDB
Streams
HotHotWarm
Fast
Stream
SearchSQLNoSQLCacheFileMessageStream
Amazon EC2
Mobile apps
Web apps
Devices
Messaging
Message
Sensors &
IoT platforms
AWS IoT
Data centers
AWS Direct
Connect
AWS Import/Export
Snowball
Logging
Amazon
CloudWatch
AWS
CloudTrail
RECORDS
DOCUMENTS
FILES
MESSAGES
STREAMS
Amazon QuickSight
Apps & Services
Analysis&visualizationNotebooksIDEAPI
Reference architecture
LoggingIoTApplicationsTransportMessaging ETL
Amazon EMR
Amazon SQS apps
Amazon Redshift
Amazon
Machine Learning
Presto
Amazon
EMR
FastSlow
Amazon EC2
Amazon Athena
BatchMessageInteractiveML

Building a Data Lake on AWS
Athena

Building a Big Data Application
web clients
mobile clients
DBMS
Amazon Redshift
AWS Cloudcorporate data center
Building a data warehouse with Amazon Redshift

web clients
mobile clients
DBMS
Amazon Redshift
AWS cloudcorporate data center
Migrating your data to AWS
AWS Database
Migration Service
AWS Direct Connect
AWS
Snowball/Snowmobile

web clients
mobile clients
DBMS
Amazon Redshift
Amazon
QuickSight
AWS Cloudcorporate data center
Visualizing your data with Amazon QuickSight
AWS Database
Migration Service
AWS Direct Connect
AWS Import/Export
& Snowball

What if your data isn’t structured?
What if you don’t need all the raw data?
What if you need to combine multiple data sets?

web clients
mobile clients
DBMS
Amazon Redshift
Amazon
QuickSight
AWS Cloud
Event-driven transformations with AWS Lambda
corporate data center
AWS Lambda
Structured Data
Amazon S3
Raw data
Amazon S3

How will this work at scale?
What if the data processing exceeds the timeout?

web clients
mobile clients
DBMS
Amazon Redshift
Amazon
QuickSight
AWS Cloud
Amazon EMR Structured Data
Amazon S3
Raw data
Amazon S3
ETL at Scale with Amazon EMR

What about ad-hoc queries when you are
exploring new data?

Extending your Data Warehouse to S3 with Amazon Athena
web clients
mobile clients
DBMS
Raw data
Amazon S3
Amazon Redshift
Staging Data
Amazon S3
Amazon
QuickSight
AWS Cloud
Amazon
EMR
Amazon
Athena

Extending your Data Warehouse to S3 with Amazon Athena
web clients
mobile clients
DBMS
Amazon Redshift
Amazon
QuickSight
AWS Cloud
Amazon
EMR
Orc/Parquet in Amazon S3
(Columnar Data Format)
Amazon
EMR
Raw data
Amazon S3
Staging Data
Amazon S3
Amazon
Athena

What if I want to run custom code or
multiple frameworks?

Extending your Data Warehouse to S3 with Apps on Amazon EMR
web clients
mobile clients
DBMS
Amazon Redshift
Orc/Parquet in Amazon S3
Amazon
QuickSight
AWS Cloud
Amazon
EMR
Amazon
EMR
Amazon
EMR
Raw data
Amazon S3
Staging Data
Amazon S3

What about real-time data?

web clients
mobile clients
DBMS
Amazon Redshift
Orc/Parquet
Amazon
QuickSight
Amazon Kinesis
Streams
AWS Cloud
Adding a real-time layer with Amazon Kinesis + Spark on Amazon EMR
Amazon
EMR
Amazon
EMR
Amazon
EMR
Raw data
Amazon S3
Staging Data
Amazon S3
Amazon
Athena

web clients
mobile clients
DBMS
Amazon Redshift
Amazon
QuickSight
AWS Cloud
Reacting to real-time data with Amazon Kinesis Analytics and AWS Lambda
Amazon Kinesis
Firehose
Amazon Kinesis
Analytics
AWS Lambda
Amazon Kinesis
Streams
Amazon SNS
Reference data
Amazon S3
Amazon
Athena

web clients
mobile clients
DBMS
Amazon Redshift
Amazon
QuickSight
AWS Cloud
Reacting in real-time and serving data with Amazon DynamoDB
Amazon Kinesis
Firehose
Amazon Kinesis
Analytics
AWS Lambda
Amazon Kinesis
Streams
Reference data
Amazon S3
Amazon
DynamoDB
Amazon SNS
Amazon
Athena

When would you use machine learning?

Amazon
DynamoDB
web clients
mobile clients
DBMS
Amazon Redshift
Amazon
QuickSight
AWS Cloud
Reacting intelligently in real-time with Amazon Machine Learning
Amazon Kinesis
Firehose
Amazon Kinesis
Analytics
AWS Lambda
Amazon Kinesis
Streams
Reference data
Amazon S3
Amazon
Machine Learning
Amazon SNS
Amazon
Athena

The Evolution of Big Data Processing
Descriptive Real-time Predictive
Dashboards;
Traditional query &
reporting
Clickstream analysis;
Ad bidding;
streaming data
Inventory forecasting;
Fraud detection;
Recommendation
engines
What happened
before
What’s happening
now
Probability of “x”
happening
Amazon Kinesis
Amazon EC2
AWS Lambda
Amazon DynamoDB
Amazon EMR
Amazon Athena
Amazon Redshift
Amazon QuickSight
Amazon EMR
Amazon Machine Learning
Amazon EMR

Q & A

Introduction to AI/ML

Amazon Confidential
A Flywheel For Data

Amazon Confidential
A Flywheel For Data
Better Products

Amazon Confidential
A Flywheel For Data
Better ProductsMore Users

Amazon Confidential
A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data

Amazon Confidential
A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch

Amazon Confidential
A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
Artificial
Intelligence

Amazon Confidential
A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
Artificial
Intelligence
Pinpoint

Machine learning and smart applications
Machine learning is the technology that
automatically finds patterns in your data and
uses them to make predictions for new data
points as they become available

Machine learning and smart applications
Machine learning is the technology that
automatically finds patterns in your data and
uses them to make predictions for new data
points as they become available
Your data + machine learning = smart applications

Smart applications by example
Based on what you
know about the user:
Will they use your
product?

Based on what you
Will they use your
product?
Based on what you
know about an order:
Is this order
fraudulent?

Based on what you
Will they use your
product?
Based on what you
know about an order:
Is this order
fraudulent?
Based on what you know
about a news article:
What other articles are
interesting?

And a few more examples…
Fraud detection Detecting fraudulent transactions, filtering spam emails,
flagging suspicious reviews, …
Personalization Recommending content, predictive content loading,
improving user experience, …
Targeted marketing Matching customers and offers, choosing marketing
campaigns, cross-selling and up-selling, …
Content classification Categorizing documents, matching hiring managers and
resumes, …
Churn prediction Finding customers who are likely to stop using the
service, upgrade targeting, …
Customer support Predictive routing of customer emails, social media
listening, …

Introducing Amazon Machine Learning
Easy-to-use, managed machine learning
service built for developers
Robust, powerful machine learning
technology based on Amazon’s internal
systems
Create models using your data already
stored in the AWS Cloud
Deploy models to production in seconds

Easy-to-use and developer-friendly
Use the intuitive, powerful service console to build
and explore your initial models
• Data retrieval
• Model training, quality evaluation, fine-tuning
• Deployment and management
Automate model lifecycle with fully featured APIs and
SDKs
• Java, Python, .NET, JavaScript, Ruby, PHP
Easily create smart iOS and Android applications with
AWS Mobile SDK

Powerful machine learning technology
Based on Amazon’s battle-hardened internal systems
Not just the algorithms:
• Smart data transformations
• Input data and model quality alerts
• Built-in industry best practices
Grows with your needs
• Train on up to 100 GB of data
• Generate billions of predictions
• Obtain predictions in batches or in real time

Integrated with AWS data ecosystem
Access data that is stored in Amazon S3,
Amazon Redshift, or MySQL databases in
Amazon RDS
Output predictions to S3 for easy integration
with your data flows
Use AWS Identity and Access Management
(IAM) for fine-grained data-access permission
policies

Fully managed model and prediction services
End-to-end service, with no servers to provision
and manage
One-click production model deployment
Programmatically query model metadata to
enable automatic retraining workflows
Monitor prediction usage patterns with Amazon
CloudWatch metrics

Three supported types of predictions
Binary classification
Predict the answer to a Yes/No question
Multiclass classification
Predict the correct category from a list
Regression
Predict the value of a numeric variable

Amazon Confidential
At Amazon (1995)

Amazon Confidential
Artificial Intelligence At Amazon
Thousands Of Employees Across The Company Focused on AI
Discovery &
Search
Fulfilment &
Logistics
Enhance
Existing Products
Define New
Categories Of
Products
Bring Machine
Learning To All

AWS is the Center of Gravity for

Amazon Confidential
The Advent Of
Deep Learning
Algorithms

Amazon Confidential
The Advent Of
Deep Learning
Data
Algorithms

Amazon Confidential
The Advent Of
Deep Learning
Data
GPUs
& Acceleration
Algorithms

Amazon Confidential
The Advent Of
Deep Learning
Data
GPUs
& Acceleration
Programming
models
Algorithms

Amazon Confidential
Can We Help Customers Put Intelligence At The
Heart Of Every Application & Business?

AI Services
AI Platform
AI Engines
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
Apache
MXNet
TensorFlow Caffe Theano KerasTorch CNTK
Amazon AI: Democratized Artificial Intelligence
Amazon
Machine Learning
EMR & Spark

AWS Deep Learning AMI: One-Click Deep Learning
Up To 40,000
CUDA Cores
Apache
MXNet
Python 3 Notebooks
& Examples
(and others)

• Flexible - Supports both imperative and symbolic programming
• Portable - Runs on CPUs or GPUs, on clusters, servers, desktops, or
mobile phones
• Multiple Languages - C++, Python, R, Scala, Julia, Matlab and
Javascript, Perl
• Distributed on Cloud - Supports distributed training on multiple
CPU/GPU machines
• Performance Optimized - Optimized C++ backend engine
parallelizes both I/O and computation
• Broad Model Support - CNN, RNN/LSTM

AI Applications on AWS
Zillow
• Zestimate (using Apache Spark)
Howard Hughes Corp
• Lead scoring for luxury real estate purchase
predictions
FINRA
• Anomaly detection, sequence matching,
regression analysis, network/tribe analysis
Netflix
• Recommendation engine
Pinterest
• Image recognition search
Fraud.net
• Detect online payment fraud
DataXu
• Leverage automated & unattended ML at
large scale (Amazon EMR + Spark)
Mapillary
• Computer vision for crowd sourced maps
Hudl
• Predictive analytics on sports plays
Upserve
• Restaurant table mgmt & POS for
forecasting customer traffic
TuSimple
• Computer Vision for Autonomous Driving
Clarifai
• Computer Vision APIs

AI Applications on AWS
Pinterest Lens
Netflix Recommendation Engine

Amazon Confidential
Amazon AI
Intelligent Services Powered By Deep Learning

Amazon Confidential
Amazon AI: Deep Learning Services
Rekognition Lex
Life-like Speech Image Analysis Conversational
Engine
Polly

Amazon Confidential
Lex: Build Natural, Conversational Interactions In Voice & Text
Voice & Text
“Chatbots”
Powers
Alexa
Voice interactions
on mobile, web
& devices
Text interaction
with Slack & Messenger
Enterprise
Connectors
(with more coming) Salesforce
Microsoft Dynamics
Marketo
Zendesk
Quickbooks
Hubspot

Amazon Confidential
The Advent Of Conversational Interactions
1st Gen: Machine-oriented
interactions

Amazon Confidential
interactions
2nd Gen: Control-oriented
& translated

Amazon Confidential
interactions
2nd Gen: Control-oriented
& translated
3rd Gen:
Intent-oriented

Amazon Confidential
Origin
Destination
Departure Date
Flight Booking

Amazon Confidential
Origin
Destination
Departure Date
Flight Booking
“Book a flight to
London”

Amazon Confidential
Origin
Destination
Departure Date
Flight Booking
“Book a flight to
London”
Automatic
Speech Recognition
Book Flight
London

Amazon Confidential
Origin
Destination
Departure Date
Flight Booking
“Book a flight to
London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Intent/
Slot Model
Utterances
Flight booking
London Heathrow

Amazon Confidential
Origin
Destination London Heathrow
Departure Date
Flight Booking
“Book a flight to
London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Intent/
Slot Model
Utterances
Flight booking
London Heathrow

Amazon Confidential
Origin Seattle
Destination London Heathrow
Departure Date
Flight Booking
“Book a flight to
London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Utterances
Flight booking
London Heathrow
Prompt
LocationLocation
“When would you like to fly?”
Intent/
Slot Model

Amazon Confidential
“Book a flight to
London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Utterances
Flight booking
London Heathrow
Prompt
LocationLocation
Polly
Intent/
Slot Model
Flight Booking

Amazon Confidential
“Next Friday”
“When would you like to
fly?”
Flight Booking

Amazon Confidential
“Next Friday”
Automatic
Speech Recognition
Natural Language
Understanding
Next Friday
Grammar
Graph
Utterances
Knowledge
Graph
Flight booking
6/9/2017
Flight Booking

Amazon Confidential
“Next Friday”
Automatic
Speech Recognition
Natural Language
Understanding
Next Friday
Grammar
Graph
Utterances
Knowledge
Graph
Flight booking
6/9/2016
“Your flight is booked for next Friday”
Confirmation
“Your flight is booked for next Friday”
Polly
Flight Booking

Amazon Confidential
Amazon AI: Three New Deep Learning Services
Polly Rekognition Lex
Life-like Speech Image Analysis Conversational
Engine

Amazon Confidential
Polly: Life-like Speech Service
Converts text
to life-like speech
47 voices 27 languages Low latency,
real time
Fully managed

Amazon Confidential
“Today in Seattle, WA, it’s 11°F”
‘"We live for the music" live from the Madison Square Garden.’
1. Automatic, Accurate Text Processing
Polly: A Focus On Voice Quality & Pronunciation

Amazon Confidential
2. Intelligible and Easy to Understand

Amazon Confidential
3. Add Semantic Meaning to Text
“Richard’s number is 2122341237“
“Richard’s number is 2122341237“
Telephone Number

Amazon Confidential
3. Add Semantic Meaning to Text
4. Customized Pronunciation
“My daughter’s name is Kaja.”
“My daughter’s name is Kaja.”

Amazon Confidential
Rekognition: Search & Understand Visual Content
Real-time &
batch image
analysis
Object & Scene
Detection
Facial Detection Face SearchFacial Analysis

Amazon Confidential
Rekognition: Object & Scene Detection
Bay
Beach
Coast
Outdoors
Sea
Water
Palm_tree
Plant
Tree
Summer
Landscape
Nature
Hotel
99.18%
99.18%
99.18%
99.18%
99.18%
99.18%
99.21%
99.21%
99.21%
58.3%
51.84%
51.84%
51.24%
Category Confidence

Amazon Confidential
Rekognition: Facial Detection

Amazon Confidential
Rekognition: Facial Analysis
Emotion: calm: 73%
Sunglasses: false (value: 0)
Gender: female (value: 0)
Mouth open wide: 0% (value: 0)
Eye closed: open (value: 0)
Glasses: no glass (value: 0)
Mustache: false (value: 0)
Beard: no (value: 0)

Amazon Confidential
Rekognition: Compare Faces

Amazon Confidential
Rekognition: Facial Search
Facial
verification
Face
Search
Visual Similarity
Search
(compare two faces) (compare many faces) (find similar faces)

Women in Big Data

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