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Machine Learning at the IoT Edge
David Nunnerley
AWS Senior Manager
AWS IoT Greengrass
I O T 2 1 4
Nobutaka Nakazawa
CTO
Brains Technology, Inc.
Masanori Sato
Group Manager
Aisin AW LTD
3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Agenda
Why Machine Learning (ML) at the Edge?
AWS IoT Greengrass overview
ML Inference at the Edge with AWS IoT Greengrass
New AWS IoT Greengrass ML capabilities
Customer use case: Aisin AW (Masanori Sato)
Brains Technology (Nobutaka Nakazawa)
4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Medical equipment Industrial machinery Extreme environments
Most machine data never reaches the cloud
6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Why this problem isn’t going away
Law of physics Law of economics Law of the land
7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS IoT Greengrass
9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS IoT Greengrass
All AWS Cloud services
e.g., Amazon S3,
Amazon Kinesis,
Amazon Redshift…
AWS IoT services
e.g., AWS IoT Core,
AWS IoT Analytics,
AWS IoT Device
Defender…
10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data and
State Sync Security
Over the
Air UpdatesConnectors
Local
Device Shadows
Code
Deployment
Lambda
Functions
AWS-grade
security
Easily Update
Greengrass Core
Machine
Learning
Inference
Local Execution
of ML Models
Local
Resource
Access
Lambdas Interact
With Peripherals
Easy integrations
with AWS
services, protocol
adaptors and
other SaaS
providers
Local
Messages
and Triggers
Local
Message Broker
Manage
Secrets at
the edge
AWS Secrets
Manager
functionality
at edge
AWS Greengrass
Extend AWS IoT to the Edge
11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data and
State Sync Security
Over the
Air UpdatesConnectors
Local
Device Shadows
Code
Deployment
Lambda
Functions
AWS-grade
security
Easily Update
Greengrass Core
Machine
Learning
Inference
Local Execution
of ML Models
Local
Resource
Access
Lambdas Interact
With Peripherals
Easy integrations
with AWS
services, protocol
adaptors and
other SaaS
providers
Local
Messages
and Triggers
Local
Message Broker
Manage
Secrets at
the edge
AWS Secrets
Manager
functionality
at edge
AWS Greengrass
Extend AWS IoT to the Edge
12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data and
State Sync Security
Over the
Air UpdatesConnectors
Local
Device Shadows
Code
Deployment
Lambda
Functions
AWS-grade
security
Easily Update
Greengrass Core
Machine
Learning
Inference
Local Execution
of ML Models
Local
Resource
Access
Lambdas Interact
With Peripherals
Easy integrations
with AWS
services, protocol
adaptors and
other SaaS
providers
Local
Messages
and Triggers
Local
Message Broker
Manage
Secrets at
the edge
AWS Secrets
Manager
functionality
at edge
AWS Greengrass
Extend AWS IoT to the Edge
13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data and
State Sync Security
Over the
Air UpdatesConnectors
Local
Device Shadows
Code
Deployment
Lambda
Functions
AWS-grade
security
Easily Update
Greengrass Core
Machine
Learning
Inference
Local Execution
of ML Models
Local
Resource
Access
Lambdas Interact
With Peripherals
Easy integrations
with AWS
services, protocol
adaptors and
other SaaS
providers
Local
Messages
and Triggers
Local
Message Broker
Manage
Secrets at
the edge
AWS Secrets
Manager
functionality
at edge
AWS Greengrass
Extend AWS IoT to the Edge
14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data and
State Sync Security
Over the
Air UpdatesConnectors
Local
Device Shadows
Code
Deployment
Lambda
Functions
AWS-grade
security
Easily Update
Greengrass Core
Machine
Learning
Inference
Local Execution
of ML Models
Local
Resource
Access
Lambdas Interact
With Peripherals
Easy integrations
with AWS
services, protocol
adaptors and
other SaaS
providers
Local
Messages
and Triggers
Local
Message Broker
Manage
Secrets at
the edge
AWS Secrets
Manager
functionality
at edge
AWS Greengrass
Extend AWS IoT to the Edge
15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data and
State Sync Security
Over the
Air UpdatesConnectors
Local
Device Shadows
Code
Deployment
Lambda
Functions
AWS-grade
security
Easily Update
Greengrass Core
Machine
Learning
Inference
Local Execution
of ML Models
Local
Resource
Access
Lambdas Interact
With Peripherals
Easy integrations
with AWS
services, protocol
adaptors and
other SaaS
providers
Local
Messages
and Triggers
Local
Message Broker
Manage
Secrets at
the edge
AWS Secrets
Manager
functionality
at edge
AWS Greengrass
Extend AWS IoT to the Edge
16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data and
State Sync Security
Over the
Air UpdatesConnectors
Local
Device Shadows
Code
Deployment
Lambda
Functions
AWS-grade
security
Easily Update
Greengrass Core
Machine
Learning
Inference
Local Execution
of ML Models
Local
Resource
Access
Lambdas Interact
With Peripherals
Easy integrations
with AWS
services, protocol
adaptors and
other SaaS
providers
Local
Messages
and Triggers
Local
Message Broker
Manage
Secrets at
the edge
AWS Secrets
Manager
functionality
at edge
AWS Greengrass
Extend AWS IoT to the Edge
17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data and
State Sync Security
Over the
Air UpdatesConnectors
Local
Device Shadows
Code
Deployment
Lambda
Functions
AWS-grade
security
Easily Update
Greengrass Core
Machine
Learning
Inference
Local Execution
of ML Models
Local
Resource
Access
Lambdas Interact
With Peripherals
Easy integrations
with AWS
services, protocol
adaptors and
other SaaS
providers
Local
Messages
and Triggers
Local
Message Broker
Manage
Secrets at
the edge
AWS Secrets
Manager
functionality
at edge
AWS Greengrass
Extend AWS IoT to the Edge
18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data and
State Sync Security
Over the
Air UpdatesConnectors
Local
Device Shadows
Code
Deployment
Lambda
Functions
AWS-grade
security
Easily Update
Greengrass Core
Machine
Learning
Inference
Local Execution
of ML Models
Local
Resource
Access
Lambdas Interact
With Peripherals
Easy integrations
with AWS
services, protocol
adaptors and
other SaaS
providers
Local
Messages
and Triggers
Local
Message Broker
Manage
Secrets at
the edge
AWS Secrets
Manager
functionality
at edge
AWS Greengrass
Extend AWS IoT to the Edge
19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data and
State Sync Security
Over the
Air UpdatesConnectors
Local
Device Shadows
Code
Deployment
Lambda
Functions
AWS-grade
security
Easily Update
Greengrass Core
Machine
Learning
Inference
Local Execution
of ML Models
Local
Resource
Access
Lambdas Interact
With Peripherals
Easy integrations
with AWS
services, protocol
adaptors and
other SaaS
providers
Local
Messages
and Triggers
Local
Message Broker
Manage
Secrets at
the edge
AWS Secrets
Manager
functionality
at edge
AWS Greengrass
Extend AWS IoT to the Edge
20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Inference Training
Machine Learning at the Edge
Local
actions
Edge Cloud
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Inference Training
Machine Learning at the Edge
Local
actions
Edge Cloud
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Inference Training
Machine Learning at the Edge
Local
actions
Edge Cloud
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Inference Training
Machine Learning at the Edge
Local
actions
Edge Cloud
25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Inference Training
Machine Learning at the Edge
Local
actions
Edge Cloud
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Inference Training
Machine Learning at the Edge
Local
actions
Edge Cloud
27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS IoT Greengrass Core Machine Learning
Run ML Inference on Greengrass
Deploy an ML model from Amazon SageMaker in the cloud to a target AWS Greengrass core
device using the Greengrass console or Command Line Interface (AWS CLI)
Install the necessary run-time for the model e.g., (TensorFlow, Apache MXNet,
Chainer…) on the AWS Greengrass core
Available for multiple hardware architectures:
e.g., Intel x86-64, ARM v7 and Nvidia Jetson TX2
Code your Lambda to read from attached device/sensor (optionally from MQTT topic) and
pass to the Lambda running the ML model. Take action based upon the inference.
28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS IoT Greengrass Core 1.7 Machine Learning
New Machine Learning capabilities
Image Classification Connector (available for download from console)
Pre-built Lambda to run the Image classification ML model
Easy coding to bridge from input device to the supplied Lambda running the
inference
Image Classification Model can be trained to learn new image classifications in the
cloud with Amazon SageMaker
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Code to use the Image Classification Connector
import greengrass_machine_learning_sdk as ml
with open('/test_img/test.jpg', 'rb') as f:
content = f.read()
def infer():
logging.info('invoking Greengrass ML Inference service')
try:
resp = client.invoke_inference_service(
AlgoType='image-classification',
ServiceName='imageClassification',
ContentType='image/jpeg',
Body=content
)
except ml.GreengrassInferenceException as e:
logging.info('inference exception {}("{}")'.format(e.__class__.__name__, e))
return
except ml.GreengrassDependencyException as e:
logging.info('dependency exception {}("{}")'.format(e.__class__.__name__, e))
return
logging.info('resp: {}'.format(resp))
predictions = resp['Body'].read()
logging.info('predictions: {}'.format(predictions))
30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Code to use the Image Classification Connector
import greengrass_machine_learning_sdk as ml
with open('/test_img/test.jpg', 'rb') as f:
content = f.read()
def infer():
logging.info('invoking Greengrass ML Inference service')
try:
resp = client.invoke_inference_service(
AlgoType='image-classification',
ServiceName='imageClassification',
ContentType='image/jpeg',
Body=content
)
except ml.GreengrassInferenceException as e:
logging.info('inference exception {}("{}")'.format(e.__class__.__name__, e))
return
except ml.GreengrassDependencyException as e:
logging.info('dependency exception {}("{}")'.format(e.__class__.__name__, e))
return
logging.info('resp: {}'.format(resp))
predictions = resp['Body'].read()
logging.info('predictions: {}'.format(predictions))
31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Code to use the Image Classification Connector
import greengrass_machine_learning_sdk as ml
with open('/test_img/test.jpg', 'rb') as f:
content = f.read()
def infer():
logging.info('invoking Greengrass ML Inference service')
try:
resp = client.invoke_inference_service(
AlgoType='image-classification',
ServiceName='imageClassification',
ContentType='image/jpeg',
Body=content
)
except ml.GreengrassInferenceException as e:
logging.info('inference exception {}("{}")'.format(e.__class__.__name__, e))
return
except ml.GreengrassDependencyException as e:
logging.info('dependency exception {}("{}")'.format(e.__class__.__name__, e))
return
logging.info('resp: {}'.format(resp))
predictions = resp['Body'].read()
logging.info('predictions: {}'.format(predictions))
32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS IoT Greengrass Core 1.7 Machine Learning
Other New Machine Learning capabilities
Greengrass support for the new Amazon SageMaker Neo (Deep Learning Runtime)
Optimize the model using Neo compiler in the cloud
More performant without loss of accuracy
Smaller memory footprint
Deploy optimized Neo model to the Greengrass core device
Install Neo run-time to the device
Write a Lambda to run the Neo optimized ML model
34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Camera
Smart vending machine
Object Detection and
Image Classification
models
Load Sensors readings
in local time series
database
Sensor fusion functions
to detect removed items
and strange objects
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3x Water Bottles
USD 1.50 each
Your total is $4.50
35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Our Launch Partners
“The addition of AWS IoT Greengrass with its latest ML Inference update running on ADLINK’s industrial vision systems makes for truly plug-
and-play IoT. Now when we power-on an off-the-shelf ADLINK NEON smart camera running AWS IoT Greengrass with its latest ML Inference
update, we can get to high-quality outcomes much, much faster. This allows us to further speed development of our IoT digital experiments
for our logistics, quality inspection, industrial robotics, and other manufacturing customers.”
- Elizabeth Campbell, General Manager, The Americas, ADLINK Technology
“The potential of computer vision use cases enabled by IoT and AI is vast for businesses to exponentially improve productivity and efficiency.
In this time of intelligent transformation, our premium industrial Think IoT cameras powered by AWS IoT Greengrass with the latest machine
learning upgrades are engineered to make a notable difference to enterprise customers.”
- Jon Pershke, Vice President of Strategy and Emerging Business, Intelligent Devices
“The pervasiveness of artificial intelligence and the pace of digital transformation continues to grow at an astonishing rate. Innovations
like the newest improvements to AWS IoT Greengrass Machine Learning that markedly decrease latency without decreasing the accuracy
of ML inference accelerate new solutions to emerging industrial automation use cases for object identification and classification. AWS’ new
machine learning solution integrated with Leopard Imaging’s AICam powered by NVIDIA® GPU will be a cornerstone in any edge to cloud
Industrial and Smart City solution.”
-Bill Pu, President and Co-Founder, Leopard Imaging
“Vieureka of Panasonic is very pleased to utilize the application evolving functions of AWS’s machine learning as enabled by AWS IoT
Greengrass. In order to offer Vieureka-Cameras and service management functions to all the partners of the AWS community, I would
like to develop a Greengrass compatible version as soon as possible. We will create the environment for developers in the spring of
2019, with commercial versions available in autumn of the same year.”
- Miyazaki, CEO of Vieureka Service, Panasonic
36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Nobutaka Nakazawa
Brains Technology, Inc.
CTO
Masanori Sato
Aisin AW LTD
Group Manager
37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine state monitoring by
cloud & edge computing
AISIN AW CO.,LTD.
Manufacturing Engineering Development
Production System Innovation Group
Masanori Sato
38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Agenda
・ Company profile
・ About our production engineering
・ Action background
・ Action summary
39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
9,830,000
NAVI
Securing a Top Share of the Global Market with Our Innovative Manufacturing
World No.1
World No.2
AT
■ Business summary of 2017(in March, 2018)
Unit sales
AT: 9,830,000units
NAVI: 1,810,000units
Sales
amount
A connection:
1,621,200 million yen
AISIN AW CO.,LTD Company Profile
40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Mission of manufacturing engineering
Workplace skills that bringing value: Production engineering
Production
◆ Finish of the SE ※ / drawing
※ Simultaneous Engineering
◆ Design of the product line / setup
◆ Design of facilities / production
◆ Development of the new production
engineering
◆ Plan, design of the factory
Ordering,
suggestion
Three-Pillar
Manufacturing
Suggestion
Suggestion
Suggestion
Production engineering
Product
Design
Trading
company
Equipment
manufacturer
Delivery of goods,
suggestion
Cooperate as a partner
Vender
41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Background of the action
Way of thinking of Industrial IoT in AISIN AW
Man Machine Material Method
Human factor
from who involved
・ Assembling
・ Machine setting
・ ・・etc.
Factor hardware such as
machines
・ Blade tool
・ Metal mold
・ ・・
etc.
Factor from Materials
(property value)
・ Ductility, toughness
・ Hardness
・ ・・etc.
Factor from
production method
・ Processing method
・ Processing order
・ ・・etc.
Building a base of high level condition monitoring and control by using information technology
The production revolution by IT is proposed in the world
→ Need to develop the Industrial IoT production system for AISIN AW
42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Background of the action
What we need for I-IoT
We have started to develop I-IoT system that utilizes "cloud & edge" that
can satisfy these requirements
• Small start
• Real-time detection
• Scalability
-Connectable with more than 20,000 machines
-Easy deployment to each factory
• Successful partner
-Quick and challenge
43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cloud & edge computing analysis base
◯◯◯ Factory
◯◯◯ Factory
Analysis
monitor
Edge device AWS Services
- Storage, Managed Services, etc
Cloud
Edge device
• Dashboard
• Simulation model making
• Algorithm development
• Edge device management
Factory ANotice
monitor
Real-time monitoring &
ML detection
Factory B
Analysis
monitor
Notice
monitor
Machines
Machines
44. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cause inquire-able detection ML algorithm
Develop machine learning algorithm that person can understand a results and can improve immediately
If not If cause is clear
Data A
Machine
learning
Not good
Data A
Machine
learning
It’s not red enough,
and It is not ripe
What is ?
What part ?
I see!
I can take action
immediately
Not good
[No reason] [Reason]
45. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cause inquire-able detection ML algorithm
Good point
・ Got good result in a month after using
→Because satisfaction of detection result, it‘s actively used
→ Leads to expansion
・The model can be constructed with high precision at an early stage
→Model making took three months ⇒ one week
Difficult point
・Since it’s new initiative, it will not be adopted unless it is indicated by the result
→Need one year temporary use for the use at the mass production line
46. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Outcomes of cloud & edge system in AISIN AW
Development of the I-IoT future
・ Further high precision monitoring by algorithm development
・ Expanding to other factories and processes and supervising management
・ Training of workers to increase IoT talent
Time[msec] Time[msec]
value
value
unusual point
The system detected “anomaly”
state and Suppress the cost of
long line stop
47. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine Learning at the IoT Edge
Nobutaka Nakazawa
CTO
Brains Technology, Inc.
I O T 2 1 4
48. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Agenda
Company profile
Overview of impulse
Algorithm
Summary
49. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
50. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Company profile
Company name Brains Technology, Inc.
Founded August 8, 2008
CEO Sawako Hamanaka
Capital 110 million yen (Including capital reserve)
Address
Shinagawa Center Building 3-23-17, Takanawa, Minato-Ku, TOKYO,
Japan
URL https://www.brains-tech.co.jp
Provide innovative service and bring about technological innovations with open technology
Providing innovative service for business enterprises, improve the productivity of corporate
activities dramatically
Our Mission
51. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
52. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Overview of Impulse
20
41
84
0
20
40
60
80
100
FY2015
(〜2016/7)
FY2016
(〜2017/7)
FY2017
(〜2018/7)
145+
Predictive Maintenance Quality Management
- Plant equipment(power, chemical, bio)
- Co-generation system
- Industrial machinery (robots)
- Construction machines (crane, elevator)
- Electrical equipment (air conditioners, water
heaters)
- Auto parts (transmissions, gears, drive shafts,
bumpers)
- Electrical equipment (LED)
- Chemical products
- Casting
Impulse is the IoT ML edge platform for the manufacturing industry
for any kind of time series data built on top of AWS.
https://impulse-cloud.com
53. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Architecture
File
Monitor
Output
result
Raw data
Anomaly
Detection
Post
Process
Factory
AWS IoT
Amazon S3
AWS Lambda Amazon
DynamoDB
AWS Batch
UI
Dashboard
Simulation
Line A
Output
result
Line B
Raw data
Edge PC
Greengrass Core
Thing
Thing
Amazon Athena
Amazon S3
Raw data
Model
Amazon SageMaker
54. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Deploy Models using ML Inference
Three modules to deploy
ML Library (MXNet, Tensorflow, scikit-learn)
AWS Greengrass ML Inference or Lambda
Your Code
Lambda
Model
AWS Greengrass ML Inference / S3
Steps to deploy
• Create AWS Lambda functions for ML
inference.
• Create Models by Amazon SageMaker or
AWS Batch and upload the models to
Amazon S3.
• AWS IoT fully manages the whole
deployment process.
Upload model files to S3
Setting up local resource in AWS IoT
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56. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Automated algorithm and parameter optimization
Analyzing the characteristics of the data and auto-selecting the optimum
algorithms and parameters
Anomaly
detection
models
Input
gaussian
periodic
correlation
independent
Mahalanobis
S-H-ESD
One Class SVM
Sparse Coding
Recommended
parameters
Breakout
LOF
Gaussian Process
Data
Characteristics
Algorithm Parameter
Output
Recommended
parameters
Recommended
parameters
Recommended
parameters
Recommended
parameters
Recommended
parameters
Recommended
parameters
57. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Sparse coding
• Sparse coding is a class of
unsupervised methods for
learning sets of over-complete
bases to represent data
efficiently
• Finds a sparse representation of
data against a fixed,
precomputed dictionary
• Works well for high-speed time-
series signals with periodic
pattern
Dictionary Leaning
Decoding from dictionary
58. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
LOF (Local Outlier Factor)
• Calculate anomaly scores
considering the density and
distance of the surrounding data
• Works well for high-dimensional
correlated data with
dimensionality reduction
technique (PCA, GPLVM, etc.)
• No need to assume a distribution
and it can be applied even when
the density has multimodality
Dimensionality reduction
LOF anomaly detection
59. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
60. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Summary
• Fully managed IoT ML edge platform
• Highly-scalable, easy to process ML leaning and model deployment cycle
• Deployment of the algorithms and the models from AWS platform can eliminate the need
to go on-site to update the algorithm or the ML model
• Lambda function with additional libraries (scikit-learn, numpy, pandas, etc.) can run any ML
logic you created. (unless exceeding Lambda size limitation)
• Some limitations still exist
• It is necessary to consider the fault tolerance at the edge
• Greengrass only runs on recent Linux environment
• Not all regions support AWS Greengrass yet
• Time series analysis needs data cache mechanism on the edge
61. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
David Nunnerley
Masanori Sato
Nobutaka Nakazawa
62. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.