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Google Cloud OnBoard 2017 Taipei

Google Cloud Platform

  • Per minute billing
  • Sustain pricing: 25% 自動提供折扣 (20% each 25% usage)
  • Compute Engine: customize CPU and memory (add more memory)
  • Committed discount (1 year or 3 year)
  • CloudNative use cases
  • Free trial 300 USD (1 year valid)

IAM

  • Google Account / Service Account / Google Groups / G suites accounts
  • Organization?

App Engine

  • Similar to AWS BeansTalk or AWS Container Service
  •  Cloud Shell / edit / preview (Very nice integration with browser!!)
  •  Standard environment / Flexible environment (provides ssh)
  •  PaaS, auto scale, container
  • Eclipse wizard integration

Cloud Datastore

  • Similar to AWS DynamoDB?
  • Encryption / Sharding / Replication
  • NoSQL 
  • Auto scaling

Billing

  • Free 28 instance hour? / cost calculator 

Cloud Storage

  • Similar with AWS S3 (bucket / region / storage type by access frequency)
  • < 5TB
  • BLOB
  • GB / per month (granular: minute)
  • Multi Regional 0.026, Regional Nearline(1 time / month)0.01, Coldline (1 time / year) 0.007

Bigtable

  • High loading read/write
  • Cloud Dataflow, Dataproc (Hadoop) integration
  • SunGard, Gmail, Google analytics

Cloud SQL

  • Similar with AWS RDS
  • MySQL 5.5 / 5.6, PostgreSQL (beta)
  • Cloud Spanner
  • Horizontally scalable
  • ACID and SQL queries, High Availability 

GKE: Container Engine

  • Kubernetes
  • Auto scaling / deployment modes (Blue/Green, Rolling Update)
  • kubctl scale / LB / expose …

Compute Engine

  • Similar to AWS EC2 but with additional customization and charging features...
  • Preemptible instance (AWS spot instance?)
  • Add template, group then add group to LB
  • Why keep mentioning pre-warm?

Google Stackdriver

  • Monitor / Trace / Logging / Report / Debugger
  • Fluentd

Lifecycle of Machine Learning model

  • Hosted TensorFlow service (!! AWS ML not provide offline SDK or framework for development)
  • Import / Export model (!! AWS ML not support this)
  • Fasten training time. (with GPU)
  • Data analyze -> clean up
  • Model might not fit the target (Asia 用餐時間 PM7 / 中東 PM9)
  • Linear Regression. Python Pandas, BQ/TnsorFlow => Predict Taxi demand from whether
  • Convolutional Neural Network => Handwriting Recognization

BigQuery

  • Datawarehouse for Analytics
  • Very interesting use case that SQL like query and see results on the fly (query duration)

Datalab

  • For data scientist
  • Very interesting use case!, Wiki style document / run python (panda) and plot chart
  • Average / RMSE 
  • Exploratory plot (whether and taxi trip count)
  • CNN => signature
  • 3 demos
  • Classification => drawing
  • Prediction => Whether and taxi trips
  • Convolution Neural Network => Handwriting reconization

Summary

  • Very similar with part of AWS services, but AWS has more complete coverage and use cases.
  • Machine Learning allow export model and based on open source TensorFlow framework
  • Billing is more flexible than AWS
  • Seems more emphasis on container use cases
  • Some special database storage, such as Cloud Spanner and BigTable, BigQuery....
  • The browser integration and UX is quite geek and interesting. (Datalab / BigQuery / Cloud Shell / Cloud Preview / In browser edit ...etc...)

References

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