Weitere ähnliche Inhalte Ähnlich wie Machine learning at the edge for industrial applications - SVC302 - New York AWS Summit (20) Mehr von Amazon Web Services (20) Machine learning at the edge for industrial applications - SVC302 - New York AWS Summit1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Machine learning at the edge for
industrial applications
Richard Elberger
Global Partner Solutions Architect, IoT
AWS
S V C 3 0 2
2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Agenda
Tenets
Industrial IoT architecture
AIoT life cycle – a four-part story
3. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Effectively maintain the system over its life cycle
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Exploit system cost-effectiveness with new intelligence
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Better decisions through central dashboards and
monitoring
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Derive value through new capabilities
8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AWS IoT Greengrass
Amazon
FreeRTOS
Amazon
FreeRTOS
Amazon
FreeRTOS
Amazon
FreeRTOS
Amazon
FreeRTOS
9. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Industrial
control
fieldbus
11. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AIoT – Machine learning at the edge
Data transport and routing
Data aggregation, enrichment,
cleansing, time series, and model config
Machine learning and model generation
Data collection and model inference
Intelligence
and outcomes
Train in the cloud and infer at the edge
AWS IoT Core AWS
Snowball
Amazon
Kinesis
AWS IoT
Analytics
Amazon EMRAmazon
S3
Amazon
SageMaker
Amazon
EC2
Amazon
SageMaker
Ground Truth
Apache
MXNet on
AWS
AWS Deep
Learning
AMIs
AWS
Snowmobile
AWS IoT
Greengrass
Amazon
FreeRTOS
AWS IoT
SiteWise
Bespoke
applications
13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AIoT – Machine learning at the edge
Data transport and routing
Data aggregation, enrichment,
cleansing, time series, and model config
Machine learning and model generation
Data collection and model inference
Intelligence
and outcomes
Train in the cloud and infer at the edge
AWS IoT Core AWS
Snowball
Amazon
Kinesis
AWS IoT
Analytics
Amazon EMRAmazon
S3
Amazon
SageMaker
Amazon
EC2
Amazon
SageMaker
Ground Truth
Xilinx
DNNDK
AWS
Snowmobile
AWS IoT
Greengrass
UltraScale+
(DPU)
Amazon
FreeRTOS
Zynq-7000
(DPU)
14. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AIoT – Machine learning at the edge
Data transport and routing
Data aggregation, enrichment,
cleansing, time series, and model config
Machine learning and model generation
Data collection and model inference
Intelligence
and outcomes
Train in the cloud and infer at the edge
AWS IoT Core AWS
Snowball
Amazon
Kinesis
AWS IoT
Analytics
Amazon EMRAmazon
S3
Amazon
SageMaker
Amazon
EC2
Amazon
SageMaker
Ground Truth
Apache
MXNet on
AWS
AWS Deep
Learning
AMIs
AWS
Snowmobile
AWS IoT
Greengrass
Amazon
FreeRTOS
AWS IoT
SiteWise
Bespoke
applications
16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
In the beginning
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Judgment on how to bring in data
18. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AIoT – Machine learning at the edge
Data transport and routing
Data aggregation, enrichment,
cleansing, time series, and model config
Machine learning and model generation
Data collection and model inference
Intelligence
and outcomes
Train in the cloud and infer at the edge
AWS IoT Core AWS
Snowball
Amazon
Kinesis
AWS IoT
Analytics
Amazon EMRAmazon
S3
Amazon
SageMaker
Amazon
EC2
Amazon
SageMaker
Ground Truth
Apache
MXNet on
AWS
AWS Deep
Learning
AMIs
AWS
Snowmobile
AWS IoT
Greengrass
Amazon
FreeRTOS
AWS IoT
SiteWise
Bespoke
applications
20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Ingesting and methodically curating
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Iteratively evaluating and refining: Where data
science meets art
Annotation
Cleansing
Data types
Re-process
raw data
22. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AIoT – Machine learning at the edge
Data transport and routing
Data aggregation, enrichment,
cleansing, time series, and model config
Machine learning and model generation
Data collection and model inference
Intelligence
and outcomes
Train in the cloud and infer at the edge
AWS IoT Core AWS
Snowball
Amazon
Kinesis
AWS IoT
Analytics
Amazon EMRAmazon
S3
Amazon
SageMaker
Amazon
EC2
Amazon
SageMaker
Ground Truth
Apache
MXNet on
AWS
AWS Deep
Learning
AMIs
AWS
Snowmobile
AWS IoT
Greengrass
Amazon
FreeRTOS
AWS IoT
SiteWise
Bespoke
applications
24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Frameworks
ML frameworks +
infrastructure
ML services
AI services
Interfaces Infrastructure
Amazon SageMaker
Amazon
Transcribe
Amazon
Polly
Amazon
Lex
Chatbots
Amazon
Rekognition
image
Amazon
Rekognition
video
Vision Speech
Amazon
Comprehend
Amazon
Translate
Languages
P3 P3dn C5 C5n Elastic inference
AWS
Inferentia
AWS IoT
Greengrass
Ground Truth Notebooks Algorithms + Marketplace RL Training Optimization Deployment Hosting
AWS Confidential - Do not Distribute
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Deep learning frameworks and toolchains
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Feeding the art: Data sets
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Training job: Compilation
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Staging
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30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AIoT – Machine learning at the edge
Data transport and routing
Data aggregation, enrichment,
cleansing, time series, and model config
Machine learning and model generation
Data collection and model inference
Intelligence
and outcomes
Train in the cloud and infer at the edge
AWS IoT Core AWS
Snowball
Amazon
Kinesis
AWS IoT
Analytics
Amazon EMRAmazon
S3
Amazon
SageMaker
Amazon
EC2
Amazon
SageMaker
Ground Truth
Apache
MXNet on
AWS
AWS Deep
Learning
AMIs
AWS
Snowmobile
AWS IoT
Greengrass
Amazon
FreeRTOS
AWS IoT
SiteWise
Bespoke
applications
31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Mixed criticality
system: Block
diagram
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Mixed criticality system: Software placement
PS PL
AWS IoT
Greengrass
33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Mixed criticality system: Pin wiring
PS PL
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Updating the model
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InferenceHandler
m2m
PS
PL
AWS IoT
Greengrass
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Device-level dependencies
InferenceHandler
m2m
PS
PL
/dev/uio0
/dev/uio1
/dev/i2c-0
/dev/i2c-1
/dev/mem
AWS IoT
Greengrass
37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Reporting inference results
ImageUploadHandler
m2m
PS
PL
AWS IoT
Greengrass
38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Staging data for building up the data lake
ImageStageHandler
m2m
PS
PL
AWS IoT
Greengrass
39. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
40. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Recap
Data transport and routing
Data aggregation, enrichment,
cleansing, time series, and model config
Machine learning and model generation
Data collection and model inference
Intelligence
and outcomes
AWS IoT Core AWS
Snowball
Amazon
Kinesis
AWS IoT
Analytics
Amazon EMRAmazon
S3
Amazon
SageMaker
Amazon
EC2
Amazon
SageMaker
Ground Truth
Apache
MXNet on
AWS
AWS Deep
Learning
AMIs
AWS
Snowmobile
AWS IoT
Greengrass
Amazon
FreeRTOS
AWS IoT
SiteWise
Bespoke
applications
41. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Richard Elberger
https://github.com/rpcme/
https://www.linkedin.com/in/richardelberger/
https://twitter.com/richardelberger