Build Smart Systems with AWS IOT, Analytics & AI

1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Neel Mitra
Solutions Architect , AWS
Sep 2017
Build Smart Systems
with AWS IOT , Analytics & AI

2. A Flywheel For Data
Machine Learning
Deep Learning
AI
More Users Better Products
More Data Better Analytics
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data

3. Three pillars of IoT
Things
Sense
& Act
Cloud
Storage
& Compute
Intelligence
Insights &
Logic → Action
AWS Greengrass

4. AWS IoT
DEVICE SDK
Set of client libraries to
connect, authenticate and
exchange messages
DEVICE GATEWAY
Communicate with devices via
MQTT and HTTP
AUTHENTICATION
Secure with mutual
authentication and encryption
RULES ENGINE
Transform messages
based on rules and
route to AWS Services
AWS Services
- - - - -
3P Services
SHADOW
Persistent thing state during
intermittent connections
APPLICATIONS
AWS IoT API
REGISTRY
Identity and Management of
your things

5. Most machine data never reaches the cloud
Medical equipment Industrial machinery Extreme environments

6. Why this problem isn’t going away
Law of physics Law of economics Law of the land

7. Things
Sense
& Act
Cloud
Storage
& Compute
Intelligence
Insights &
Logic → Action
AWS IoT
Starting in the cloud
Action
Device
State
AWS Services
Applications
Authentication
& Authorization
Device
Gateway
Registry
AWS IoT API
Messages Messages

8. Messages Messages
Authentication
& Authorization
Device
Gateway
Action
Device
State
AWS Services
Applications
Registry
AWS IoT API
AWS IoT
Going to the edge
Introducing AWS Greengrass
Device
State
Action
Device
Gateway
Messages
Authentication
& Authorization
Security
*Note: Greengrass is NOT Hardware (You bring your own)

9. Local
Lambda
Local
Device Shadows Local
Security
Greengrass
is …
AWS
Local
Broker
Greengrass is:

10. Device Patterns on AWS

11. Point to Point
SUB: vacuum/10930Device Gateway
Mobile App
{ “command”: “clean” }
PUB: vacuum/10930
Start Cleaning

12. Broadcast Pattern
SUB: cars/us_MA/weather
Device Gateway
Weather Service
{ “forecast”: “snow”
“prob”: “85%”
“geo”: [42.3,71.0] }
PUB: cars/us_MA/weather
Reduce Speed
Ignore
Reduce Speed
Connected Vehicle: https://www.youtube.com/watch?v=o1cN0KDaOf4

13. Fan Out Notification Pattern
Device Gateway
Repair Service
{ “rep-102”: “shipped” }
PUB: SN/{serial}/repair
{ “rep-111”: “delayed” }
Alert Arrival Day
Change Gear Speed
SUB: SN/SN-2390/repair
SUB: SN/SN-2289/repair
PUB: SN/SN-2390/repair
PUB: SN/SN-2289/repair

14. Aggregator Pattern
PUB: cleaning/vac-1203/home1234
PUB: cleaning/vac-1204/home1234
Device Gateway
SUB: cleaning/+/home1234
{ “vac-1203”: “cleaning” }
{ “vac-1204”: “waiting” }
{ “vac-1203”: “cleaning” }
{ “vac-1204”: “waiting” }
Alert: Cleaning
Alert: Waiting

15. Three pillars of IoT
Things
Sense
& Act
Cloud
Storage
& Compute
Intelligence
Insights &
Logic → Action
AWS Greengrass

16. Data Lake Architecture

17. Types of Data
• Database records
• Search documents
• Log files
• Messaging events
• Devices / sensors / IoT stream
Devices
Sensors &
IoT platforms
AWS IoT STREAMS
Stream
storage
IoT
COLLECT STORE
Mobile apps
Web apps
Data centers
AWS Direct
Connect
RECORDS
Database
Applications
AWS Import/Export
Snowball
Logging
Amazon
CloudWatch
AWS
CloudTrail
DOCUMENTS
FILES
Search
File store
LoggingTransport
Messaging
Message MESSAGES Queue
Messaging

18. Amazon Kinesis
Firehose
Amazon Kinesis
Streams
Apache Kafka
Amazon DynamoDB
Streams
Amazon SQS
Amazon SQS
• Managed message queue service
Apache Kafka
• High throughput distributed messaging
system
Amazon Kinesis Streams
• Managed stream storage + processing
Amazon Kinesis Firehose
• Managed data delivery
Amazon DynamoDB
• Managed NoSQL database
• Tables can be stream-enabled
Message & Stream Storage
Devices
Sensors &
IoT platforms
AWS IoT STREAMS
IoT
COLLECT STORE
Mobile apps
Web apps
Data centers
AWS Direct
Connect
RECORDS
Database
Applications
AWS Import/Export
Snowball
Logging
Amazon
CloudWatch
AWS
CloudTrail
DOCUMENTS
FILES
Search
File store
LoggingTransport
Messaging
Message MESSAGES
Messaging
Queue
Stream

19. COLLECT STORE
Mobile apps
Web apps
Data centers
AWS Direct
Connect
RECORDS
Database
AWS Import/Export
Snowball
Logging
Amazon
CloudWatch
AWS
CloudTrail
DOCUMENTS
FILES
Search
Messaging
Message MESSAGES
Devices
Sensors &
IoT platforms
AWS IoT STREAMS
Apache Kafka
Amazon Kinesis
Streams
Amazon Kinesis
Firehose
Amazon DynamoDB
Streams
Hot
Stream
Amazon S3
Amazon SQS
Message
Amazon S3
File
LoggingIoTApplicationsTransportMessaging
File Storage

20. Cache, database, search
COLLECT STORE
Mobile apps
Web apps
Data centers
AWS Direct
Connect
RECORDS
AWS Import/Export
Snowball
Logging
Amazon
CloudWatch
AWS
CloudTrail
DOCUMENTS
FILES
Messaging
Message MESSAGES
Devices
Sensors &
IoT platforms
AWS IoT STREAMS
Apache Kafka
Amazon Kinesis
Streams
Amazon Kinesis
Firehose
Amazon DynamoDB
Streams
Hot
Stream
Amazon SQS
Message
Amazon Elasticsearch
Service
Amazon DynamoDB
Amazon S3
Amazon ElastiCache
Amazon RDS
SearchSQLNoSQLCacheFile
LoggingIoTApplicationsTransportMessaging

21. Tools and Frameworks
• Machine Learning
• Amazon ML, Amazon EMR (Spark ML)
• Interactive
• Amazon Redshift, Amazon EMR (Presto, Spark)
• Batch
• Amazon EMR (MapReduce, Hive, Pig, Spark)
• Messaging
• Amazon SQS application on Amazon EC2
• Streaming
• Micro-batch: Spark Streaming, KCL
• Real-time: Amazon Kinesis Analytics, Storm,
AWS Lambda, KCL
Amazon SQS apps
Streaming
Amazon Kinesis Analytics
Amazon KCL
apps
AWS Lambda
Amazon Redshift
PROCESS / ANALYZE
Amazon Machine
Learning
Presto
Amazon
EMR
FastSlowFast
BatchMessageInteractiveStreamML
Amazon EC2
Amazon EC2

22. STORE CONSUMEPROCESS / ANALYZE
Amazon QuickSight
Apps & Services
Analysis&visualizationNotebooksIDEAPI
Applications & API
Analysis and visualization
Notebooks
IDE
Business
users
Data scientist,
developers
COLLECT ETL

23. Amazon SQS apps
Streaming
Amazon Kinesis Analytics
Amazon KCL
apps
AWS Lambda
Amazon Redshift
COLLECT STORE CONSUMEPROCESS / ANALYZE
Amazon Machine
Learning
Presto
Amazon
EMR
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
FastSlowFast
BatchMessageInteractiveStreamML
SearchSQLNoSQLCacheFileQueueStream
Amazon EC2
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

24. Design Patterns

25. On-demand Big Data Analytics

26. Data Warehousing

27. Smart Applications | Machine Learning

28. Clickstream Analysis

29. Three pillars of IoT
Things
Sense
& Act
Cloud
Storage
& Compute
Intelligence
Insights &
Logic → Action
AWS Greengrass

30. 1st Gen: Machine-oriented
interactions
2nd Gen: Control-oriented
& translated
3rd Gen:
Intent-oriented
The Advent Of Conversational Interactions

31. Artificial Intelligence
At Amazon (1995)

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

33. 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

34. Algorithms
Data
Programming
Models
GPUs &
Acceleration
The Advent of Deep Learning
image understanding
natural language
processing
speech recognition
autonomy

35. One-Click GPU
Deep Learning
AWS Deep Learning AMI
Up to~40k CUDA cores
MXNet
TensorFlow
Theano
Caffe
Torch
Pre-configured CUDA drivers
Anaconda, Python3
+ CloudFormation template
+ Container Image

36. Can We Help Customers
Put Intelligence At The Heart Of
Every Application & Business?

37. Amazon AI
Intelligent Services Powered By Deep Learning

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

39. Origin
Destination
Departure Date
Flight Booking
“Book a flight
to London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Utterances
Flight booking
London Heathrow
Intent /
Slot model
London Heathrow
LocationLocation
Seattle

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

41. Origin
Destination
Departure Date
Flight Booking
London Heathrow
Seattle
Prompt
“When would you like to fly?”
“When would you
like to fly?”
Polly
“Next Friday”

42. Origin
Destination
Departure Date
Flight Booking
“Next Friday”
Automatic
Speech Recognition
Next Friday
Utterances
Natural Language
Understanding
Flight booking
02 / 24 / 2017
Intent /
Slot model
London Heathrow
Seattle
02/24/2017

43. Origin
Destination
Departure Date
Flight Booking
“Next Friday”
Automatic
Speech Recognition
Next Friday
Utterances
Natural Language
Understanding
Flight booking
02 / 24 / 2017
Intent /
Slot model
London Heathrow
Seattle
02/24/2017
Confirmation
“Your flight is booked for next Friday”
“Your flight is booked
for next Friday”
Polly

44. Origin
Destination
Departure Date
Flight Booking
“Next Friday”
Automatic
Speech Recognition
Next Friday
Utterances
Natural Language
Understanding
Flight booking
02 / 24 / 2017
Intent /
Slot model
London Heathrow
Seattle
02/24/2017
Hotel Booking

45. Success Stories

46.  The Philips HealthSuite digital platform
analyzes and stores 15 PB of patient data
gathered from 390 million imaging
studies, medical records, and patient
inputs
 Running on AWS provides the reliability,
performance and scalability that Philips
needs to help protect patient data as its
global digital platform grows at the rate of
one petabyte per month.
AWS Customers making an impact with IoT
Video Testimonial

47. The BMW Group
 Connected-car application collects sensor
data from BMW 7 Series cars to give drivers
dynamically updated map information.
 Built its new car-as-a-sensor (CARASSO)
service in only six months
 CARASSO can adapt to rapidly changing
load requirements; can scale up and down
by two orders of magnitude within 24 hours.
 By 2018 CARASSO is expected to process
data collected by a fleet of 100,000 vehicles
traveling more than eight billion kilometers.
AWS Customers making an impact with IoT
Video Testimonial

48. “For our market surveillance
systems, we are looking at
about 40% [savings with
AWS], but the real benefits
are the business benefits:
We can do things that we
physically weren’t able to
do before, and that is
priceless.”
- Steve Randich, CIO
Analytics Case Study: Re-architecting Compliance
What FINRA needed
• Infrastructure for its market surveillance platform
• Support of analysis and storage of approximately 75 billion
market events every day
Why they chose AWS
• Fulfillment of FINRA’s security requirements
• Ability to create a flexible platform using dynamic clusters
(Hadoop, Hive, and HBase), Amazon EMR, and Amazon S3
Benefits realized
• Increased agility, speed, and cost savings
• Estimated savings of $10-20m annually by using AWS

49. • Began implementing an S3 data lake on AWS in 2014
• Has been running in production since early 2015
• Now able to integrate all data sets together in one analytics platform, i.e. sales data, marketing
data, manufacturing line data, patient population data, FDA public datasets, etc.
• Can easily share this aggregated data across all business units
• Rapid data experimentation
• Enables new use cases & data innovations not previously possible
• Leverages Amazon EMR and Amazon Redshift for their analytics layer around the lake
• Leverages R-Studio and SAS for data science layer on top of EMR and Redshift
• They use EMR for their ETL layer
• EMR is 50% faster & 30% cheaper than their legacy ETL solution
• Amgen’s AWS S3 data lake won Best Practice Award at Bio-IT World 2016 for ‘Real World Data
Platform & Analytics’
Best Practices
Awards
Bio IT World
2016
Winner

50. Thank you!