Saving Human Lives with the IoT
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Slides for my talk at Cloud Foundry Summit Europe 2016. Nearly 1.2 million people die in road crashes each year (WHO - 2015) with additional millions becoming injured or disabled. One big part of this problem is worst road traffic conditions and unless action is taken, road traffic injuries are predicted to become the fifth leading cause of death by 2030. Moreover, although road traffic injuries have been a major cause of mortality for many years, most traffic accidents are both predictable and preventable. In this talk, we want to demonstrate a scalable IoT platform that uses weather data and data from other cars to warn drivers of dangerous conditions. We will show how CF can help to save human lives and the architecture behind this. Additionally, we will also explain the data science that is involved.
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Saving Human Lives with the IoT
- 1. Saving Human Lives with the IoT Cloud Foundry Summit Europe 2016 Dat Tran @datitran September 27th, 2016
- 2. 2 echo $(whoami) Senior Data Scientist @datitran
- 3. Nearly 1.2 million people die in road crashes each year (WHO - 2015)
- 4. Worst road traffic conditions like glazed frost
- 5. Most traffic accidents are predictable and preventable
- 6. We believe transforming how the world builds software will make the world a safer place
- 7. 7 Technology
- 9. 9 Cloud Native Architecture Predictive API Redis for Pivotal CF Deep Learning Spring XD FTP Source Shell Processor Tap S3 Feature Engineering Dashboard Enricher Model Pipeline Orchestration EC2 S3Sinkforpersistentstorage Redis Sink Measured data Weather data λ-Architecture Modelpersistence JSON JSON CSVCSV CSV Real-Time Layer Batch Layer Models
- 10. 10 Spring XD Unified, distributed, and extensible open-source system for data ingestion, real time analytics, batch processing and data export o Data Ingestion and Pipeline Processing o Real Time Analytics o Rapid Dashboarding o Batch Workflow Orchestration + ETL Redis for Pivotal CFSpring XD FTP Source Shell Processor Tap S3 S3Sinkforpersistentstorage Redis Sink
- 11. 11 Batch Layer Deep Learning S3 Feature Engineering Model Pipeline Orchestration CSVCSV
- 12. 12 Real-Time Layer Predictive API Redis for Pivotal CF Dashboard Enricher Measured data Weather data JSON JSON CSV Real-Time Layer Models
- 13. 13 Pivotal Cloud Foundry PaaS more… http:// Push App> cf
- 14. Modelling
- 15. 15 Short Introduction into Deep Learning Input Layer Output Layer Hidden Layer 1 Hidden Layer 2 Positive Neutral Negative
- 16. 16 Types of Neural Network Recurrent 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 problems o Architectures: Multi-layer perceptron, CNNs etc. o Many applications in supervised learning
- 17. 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. 18 Key Learnings o Overbalanced problem o Use simple RNNs over LSTM o Use GPUs! o Time-consuming to find the optimal network o Many data is needed
- 19. 19 Processes
- 20. 20 Radically Agile Data Science @ Pivotal Labs Pair Programming Retros Test Driven Development Continuous Integration / API First Tracker Standups
- 21. 21 API First Source: What is hardcore data science—in practice? (Mikio Braun)
- 22. 22 Additional Links o API First for Data Science http://engineering.pivotal.io/post/api-first-for-data-science/ o What is hardcore data science—in practice? https://www.oreilly.com/ideas/what-is-hardcore-data- science-in-practice
- 23. 23 Continuous Delivery Release once every 6 months More Bugs in production Release early and often Higher Quality of Code Pair Programming Not my problem I pay so you deliver Shared responsibility We build it together Microservices Tightly coupled components Slow deployment cycles waiting on integrated tests teams Loosely coupled components Automated deploy without waiting on individual components
- 24. 24 Key Takeaways o API First: Bringing the models into production as fast as possible helps to minimize risk o Clients can test it and give early/regular feedback o Fast ROI o Cloud Foundry enables us to reliably expose models as scalable predictive APIs o Cloud Native Data science is crucial for Smart Apps