The document discusses using AWS IoT Analytics to create ambient intelligent environments by connecting IoT and machine learning. It presents an agenda that includes key ambient intelligence features, an introduction to AWS IoT Analytics, and an example use case of occupant-driven home HVAC automation. The objectives are to understand how AWS IoT Analytics can be used for connecting IoT data and machine learning models to create environments that are aware of context, adapt to changing situations, and anticipate needs.

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Ambient Intelligence – Bringing ML
and AI to the Connected Home
Catalin Vieru
Sr. Specialized IoT Architect
Amazon Web Services
S e s s i o n I D

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Agenda
Key Ambient Intelligence Features
AWS IoT Analytics & Ambient Intelligence Environments
Brief Intro to AWS IoT Analytics
Example Use Case: Occupant Driven Home HVAC Automation

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. . . is able to identify us and
recognizes identities . . .

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Who?
Where?
What?
. . . is aware of the
situational context . . .

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. . . always adapts to our ever-changing life . . .

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. . . is personalized and tailored to our needs . . .

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. . . and anticipates our needs
without any conscious mediation!

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Session Objectives
How can we use AWS IoT Analytics for connecting IoT and
ML to create ambient intelligent environments?
Machine learningInternet of things

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Container execution for analytical workflows

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Customizable analytical time windows
t1 t3
t2 t4

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Use Case Architecture
Temperature
schedule
Daily training dataset
Thermostat
Temperature
Sensor
Smart home
AWS IoT Analytics data pipeline
30-minute prediction dataset
Historical weather
database
Weather forecast
Personal calendar(s)

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Thank you!
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Catalin Vieru
vieruc@amazon.com