Saving Human Lives with the IoTCloud Foundry Summit Europe 2016

Dat Tran @datitranSeptember 27th, 2016

2

echo $(whoami)

Senior Data Scientist

@datitran

Nearly 1.2 million people die in road crashes each year

(WHO - 2015)

Worst road traffic conditions like glazed frost

Most traffic accidents are

predictable and preventable

We believe transforming how the world builds software will make the

world a safer place

7

Technology

8

“Cloud” Architecture

9

Cloud Native ArchitecturePredictive API

Redisfor Pivotal CF

Deep Learning

Spring XD

FTP Source Shell Processor

Tap

S3

Feature Engineering

Dashboard

Enricher

Model Pipeline Orchestration

EC2

S3 Sink for persistent storage

Redis Sink

Measured data

Weather data

λ-Architecture

Model persistence

JSON

JSON

CSVCSV

CSV

Real-Time Layer

Batch Layer

Models

10

Spring XD

Unified, distributed, and extensible open-source system for data ingestion, real time analytics, batch processing and data exporto Data Ingestion and Pipeline Processingo Real Time Analyticso Rapid Dashboardingo Batch Workflow Orchestration + ETL

Redisfor Pivotal CFSpring XD

FTP Source Shell Processor

Tap

S3

S3 Sink for persistent storage

Redis Sink

11

Batch Layer

Deep Learning

S3

Feature Engineering

Model Pipeline Orchestration

CSVCSV

12

Real-Time LayerPredictive API

Redisfor Pivotal CF

Dashboard

Enricher

Measured data

Weather data JSON

JSON

CSV

Real-Time Layer

Models

13

Pivotal Cloud Foundry PaaS

more…

http://

Push App> cf

Modelling

15

Short Introduction into Deep Learning

Input Layer Output Layer

Hidden Layer 1 Hidden Layer 2

Positive

Neutral

Negative

16

Types of Neural NetworkRecurrent Neural NetworkFeed-Forward Neural Network

o Models dynamic temporal behaviors o Many variants: LSTM, GRU, Bi-

directional RNNs etc.o Applications: Handwriting and

speech recognition and many more

o Ideal for functional mapping problemso Architectures: Multi-layer perceptron,

CNNs etc.o Many applications in supervised

learning

17

Recurrent Neural Network

Image classification

Image captioning

Sentiment analysis

Machine translation

Video classification

Source: The Unreasonable Effectiveness of Recurrent Neural Networks (Andrej Karpathy)

18

Key Learnings

oOverbalanced problem

oUse simple RNNs over LSTM

oUse GPUs!

oTime-consuming to find the optimal network

oMany data is needed

19

Processes

20

Radically Agile Data Science @ Pivotal Labs

Pair Programming

Retros

Test Driven Development

Continuous Integration / API First

Tracker

Standups

21

API First

Source: What is hardcore data science—in practice? (Mikio Braun)

22

Additional Links

oAPI First for Data Science http://engineering.pivotal.io/post/api-first-for-data-science/

oWhat is hardcore data science—in practice? https://www.oreilly.com/ideas/what-is-hardcore-data-science-in-practice

23

Continuous Delivery

Release once every 6 monthsMore Bugs in production

Release early and oftenHigher Quality of Code

Pair Programming

Not my problemI pay so you deliver

Shared responsibilityWe build it together

Microservices

Tightly coupled componentsSlow deployment cycles waiting on

integrated tests teams

Loosely coupled componentsAutomated deploy without waiting on

individual components

24

Key Takeaways

oAPI First: Bringing the models into production as fast as possible helps to minimize risk

oClients can test it and give early/regular feedback

oFast ROI

oCloud Foundry enables us to reliably expose models as scalable predictive APIs

oCloud Native Data science is crucial for Smart Apps

25

Questions?

@datitran