Google APAC Machine Learning Day 是 Google 今年三月初於新加坡 Google 辦公室針對機器學習所舉辦的兩天研討會活動，本次聚會將邀請前往參加該活動的 Evan Lin 及他的同事 Benjamin Chen 帶來他們的心得分享，內容包括: Tensorflow Summit RECAP Machine Learning Expert Day 所見所聞 分享一下 Linker Networks 如何使用 Tensorflow https://gdg-taipei.kktix.cc/events/google-apac-machine-learning-day

1. Google APAC
Machine Learning
Expert Day
Linkernetworks - Evan Lin / Benjamin Chen

2. ● Tensorflow Summit Recap
● Google APAC Machine Learning Expert Day
● Our lightening talk (Linker Neworks)
Agenda

3. Who is Evan Lin
● Daily Work:
○ Linker Networks : Cloud
Architect in
● Community:
○ Co-Organizer in Golang
Taipei User Group
● Habit:
○ Project 52

4. Tensorflow Summit RECAP

5. Tensorflow Dev Summit 2017
link

21. Benjamin Chen
Linker Networks
Data Scientist
Taiwan R User Group
Officer
benjamin0901@gmail.com

23. After 1.0.0
● 1.0.0
○ XLA
○ pip install tensorflow
○ JAVA API
● 1.1.0
○ Keras 2.0-->tf.contrib.keras
■ tf.keras by TF 1.2
○ tf.estimator

26. TensorFlow Wide & Deep Learning
Wide Model Deep Model
Memorization Generalization
Revelance Diversity

27. Deep Model
Generalization
Diversity

28. Wide Model
Memorization
Relevance

29. Wide & Deep Model

30. Classify cucumbers with tensorflow

31. Classify cucumbers with tensorflow

32. Japanese Idol with DCGAN (link)

33. APAC Machine Learning
Expert Day 1

37. Some Interesting Projects

38. Deep Learning in your flash drive (link)

39. Tensorflow example from zero to all (link)

40. Tensorflow What? (link)

41. Other lightning talks
● Context and attention extraction / modeling
● NLU and Cognitive Architectures
● [Linker Networks] When Kubernetes meets Tensorflow
● [Linker Networks] Running Distributed Tensorflow with
Jupyter Notebook and Kubernetes
● [Linker Networks] Machine Intelligent Cluster

42. APAC Machine Learning
Expert Day 2

44. Tensorflow intro with Codelab (link)

45. Google Cloud Codelab

46. Classify Manhattan

47. Classify MNIST images

48. Linker Networks
When Kubernetes meets Tensorflow

50. Machine Intelligence Cluster: Use
Tensorflow to improve Kubernetes● Goal:
○ Kubernetes with Machine
Intelligence
● Role played by ML:
○ Maximize utilization
○ Risk mitigation
● Tools Used:
○ Tensorflow
○ Spark Streaming

51. Utilization Prediction
- Product: Cluster of Machine
Intelligence, CMI
- Goals:
- Predict CPU and memory trend
- Auto-scaling
- Algorithm: LSTM
- Module: Keras
- Trying to tune threshold

52. Back to Evan

53. Eliminate engineering bottlenecks
in Machine Learning

54. Data Collect Probe & Sensor & Smart GW
Vizualization
Data Process
Data Analysis &
Machine Learning
DC/OS Spark ML Tensorflow
DC/OS
Storage
Cassandra
Kafka (Queueing)
Go/Akka (Connector)
Spark (ETL/Streaming)
D3.js
Scikit Learn R
Interactive
Dashboard
Jupyter Notebook
Zeppelin
ML Job
Scheduler
Chronos
HPC (with
GPU)
server Storage SDNStorage SDN

55. Analytics Machine Intelligence Platform (AMIP)
: Building deep learning platform on top of
Kubernetes
● Goal:
○ Zero setup for Tensorflow
(private/public cloud)
○ Migrate with Jupyter, TensorBoard
and TensorServing
● Tools Used:
○ Kubernetes
○ Tensorflow

56. AMIP Architecture

57. Linker is hiring
Cloud Platform Developer
- Familiar with DCOS/K8S
- Strong DevOps experience

58. Q&A