Slide 1

Slide 1 text

Having fun with Google Cloud + RasPi

Slide 2

Slide 2 text

+Kazunori Sato @kazunori_279 Kaz Sato Staff Developer Advocate, Tech Lead for Data & Analytics Cloud Platform, Google Inc.

Slide 3

Slide 3 text

= The Datacenter as a Computer

Slide 4

Slide 4 text

No content

Slide 5

Slide 5 text

Enterprise

Slide 6

Slide 6 text

Jupiter network 40 G ports 10 G x 100 K = 1 Pbps total CLOS topology Software Defined Network

Slide 7

Slide 7 text

Borg No VMs, pure containers Manages 10K machines / Cell DC-scale proactive job sched (CPU, mem, disk IO, TCP ports) Paxos-based metadata store

Slide 8

Slide 8 text

Google BigQuery

Slide 9

Slide 9 text

1 B 1 B 100 B 900 M

Slide 10

Slide 10 text

At Google, MapReduce is classic. We use BigQuery. Confidential & Proprietary Google Cloud Platform 10

Slide 11

Slide 11 text

Google BigQuery Demo: RegEx + GROUP BY on 10 B rows response read RegEx 10B ~10sec 372GB

Slide 12

Slide 12 text

SELECT your_data FROM billions_of_rows WHERE full_disk_scan_required = true; Scanning 1 TB in 1 sec with 5,000 - 10,000 disk spindles

Slide 13

Slide 13 text

BigQuery Analytic Service in the Cloud BigQuery Analyze Export Import How to use BigQuery? Google Analytics ETL tools Connectors Google Cloud BI tools and Visualization Google Cloud Spreadsheets, R, Hadoop

Slide 14

Slide 14 text

“The World's Largest IoT platform” with RasPi + BigQuery

Slide 15

Slide 15 text

Blazingly Fast Capable of scanning 100B rows in ~20 sec Low Cost Storage: $0.020 per GB per month Queries: $5 per TB Fully Managed Use thousands of servers with zero-ops SQL Simple and Intuitive SQL with JS UDF Benefits of BigQuery

Slide 16

Slide 16 text

Confidential & Proprietary Google Cloud Platform 16 Google Brain

Slide 17

Slide 17 text

No content

Slide 18

Slide 18 text

No content

Slide 19

Slide 19 text

● Google Now ● Google Photos ● Gmail ● YouTube ● and more Google Brain: The Brain of Google services

Slide 20

Slide 20 text

No content

Slide 21

Slide 21 text

No content

Slide 22

Slide 22 text

Confidential & Proprietary Google Cloud Platform 22 Google Cloud Vision API

Slide 23

Slide 23 text

Demo Video

Slide 24

Slide 24 text

24 Types of Detection ● Label ● Landmark ● Logo ● Face ● Text ● Safe search

Slide 25

Slide 25 text

25 Types of Detection Face Detection ○ Find multiple faces ○ Location of eyes, nose, mouth ○ Detect emotions: joy, anger, surprise, sorrow Entity Detection ○ Find common objects and landmarks, and their location in the image ○ Detect explicit content

Slide 26

Slide 26 text

Making a request { "requests":[ { "image": { "content": "base64ImageString" }, "features": [ { "type": "LABEL_DETECTION", "maxResults": 10 }, { "type": "FACE_DETECTION", "maxResults": 10 }, // More feature detection types... ] } ] }

Slide 27

Slide 27 text

Confidential & Proprietary Google Cloud Platform 27 TensorFlow

Slide 28

Slide 28 text

A new system for distributed, parallel machine learning: ● Based on general-purpose dataflow graphs ● Targeting heterogeneous devices ○ single PC with CPU ○ single PC with GPU(s) ○ mobile device ○ clusters of 100s or 1000s of CPUs and GPUs What is TensorFlow?

Slide 29

Slide 29 text

Portable ● Runs on many platforms ○ Data Center ○ CPUs and GPUs ○ Mobile phones

Slide 30

Slide 30 text

Yet another dataflow systemwith tensors MatMul Add Relu biases weights examples labels Xent Edges are N-dimensional arrays: Tensors

Slide 31

Slide 31 text

Distributed

Slide 32

Slide 32 text

Simple Example

Slide 33

Slide 33 text

Thank you