Tweet
Tweet

Slide 1

Slide 1 text

Salvando vidas com dispositivos semi-conectados Julio Faerman @faermanj julio.faerman@nubego.io

Slide 2

Slide 2 text

Julio Faerman @faermanj ● Independent Software Engineer specialized in Cloud Computing ● Can code but not solder ● Before: Amazon Web Services Developer Relations & Training ● Previously: Red Hat, Borland and Enterprise Computing in Brazil ● Today: Own thoughts and references of a maker

Slide 3

Slide 3 text

“We are no longer patients we are consumers of health.” AWS re:Invent 2014 | Philips Uses AWS to Analyze 15 PB of Patient Data

Slide 4

Slide 4 text

No content

Slide 5

Slide 5 text

No content

Slide 6

Slide 6 text

No content

Slide 7

Slide 7 text

c Brazil’s healthtech sector is new hot spot Manoel Lemos c

Slide 8

Slide 8 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T

Slide 9

Slide 9 text

Engineering for Health Today’s Solutions Personal assistants, gateways and other devices. Microprocessors, display and stable connectivity Microcontrollers and sensors Wearable monitors, mobile applications and limited conectivity

Slide 10

Slide 10 text

No content

Slide 11

Slide 11 text

Alexa Healthcare Skills

Slide 12

Slide 12 text

No content

Slide 13

Slide 13 text

No content

Slide 14

Slide 14 text

{ "body": { "version": "1.0", "response": { "outputSpeech": { "type": "PlainText", "text": "Welcome to the Alexa Nursing Skill. Please tell me your name" }, …

Slide 15

Slide 15 text

No content

Slide 16

Slide 16 text

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS IoT Button

Slide 17

Slide 17 text

No content

Slide 18

Slide 18 text

No content

Slide 19

Slide 19 text

No content

Slide 20

Slide 20 text

No content

Slide 21

Slide 21 text

No content

Slide 22

Slide 22 text

No content

Slide 23

Slide 23 text

Nordic Thingy:52 and Thingy:91

Slide 24

Slide 24 text

No content

Slide 25

Slide 25 text

No content

Slide 26

Slide 26 text

Security is Job #0

Slide 27

Slide 27 text

No content

Slide 28

Slide 28 text

No content

Slide 29

Slide 29 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T

Slide 30

Slide 30 text

No content

Slide 31

Slide 31 text

No content

Slide 32

Slide 32 text

No content

Slide 33

Slide 33 text

No content

Slide 34

Slide 34 text

AWS Lam bda / R E G I S T E R CH E CK CERT RO B O T S T O REG I S T ER D E A D L ET T E R Q U E U E CRE A T E S H A D O W P E RM I S S I O N S Am azon SQS Messages Am azon Cl oudWat ch L O G G I N G L I F E CY CL E E V E N T REG I S T ER RO B O T Q U E U E REA D ER AWS Lam bda AWS Lam b da A W S I o T Am azon API Gat ew ay I o T Sh ad o w I o T Ru l e $15k per month to run 2 IT people to support…” Leading Edge Forum 2018 “… 23M robot vacuum cleaners…

Slide 35

Slide 35 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T

Slide 36

Slide 36 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Security AWS-grade security Data and state sync Local Device Shadows Local triggers Local Message Broker AWS IoT Greengrass Features Local actions Local AWS Lambda Functions

Slide 37

Slide 37 text

Amazon Comprehend Medical Entities • Medication • Medical condition • Test, Treatments and Procedures • Anatomy • Protected Health Information (PHI) Relationship Extraction • Medication and dosage • Test and result • Many more Entity Traits • Negation • Diagnosis, Sign or Symptom Protected Health Information Identification (PHId API) Medical Named Entity and Relationship Extraction (NERe API)

Slide 38

Slide 38 text

Pt is 40yo mother, highschool teacher HPI : Sleeping trouble on present dosage of Clonidine. Severe Rash on face and leg, slightly itchy Meds : Vyvanse 50 mgs po at breakfast daily, Clonidine 0.2 mgs -- 1 and 1 / 2 tabs po qhs HEENT : Boggy inferior turbinates, No oropharyngeal lesion Lungs : clear Heart : Regular rhythm Skin : Mild erythematous eruption to hairline Follow-up as scheduled

Slide 39

Slide 39 text

No content

Slide 40

Slide 40 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T 1. Train machine learning model on the cloud 2. Deploy optimized model to target devices running on Intel or NVIDIA hardware 3. Create and accelerate inference applications on the edge AWS Greengrass ML Inference

Slide 41

Slide 41 text

https://github.com/awslabs/Amazon-Pollexy

Slide 42

Slide 42 text

No content

Slide 43

Slide 43 text

No content

Slide 44

Slide 44 text

No content

Slide 45

Slide 45 text

Bike safety application

Slide 46

Slide 46 text

Food safety application

Slide 47

Slide 47 text

No content

Slide 48

Slide 48 text

Obrigado! Julio Faerman @faermanj julio.faerman@nubego.io