The document provides a recap of the Google APAC Machine Learning Expert Day, highlighting the contributions of Evan Lin and Benjamin Chen from Linker Networks. It discusses various projects and technologies such as TensorFlow, Kubernetes, and machine intelligence platforms aimed at improving cloud architecture and machine learning processes. The event included talks on topics like natural language understanding, deep learning applications, and tools for prediction and scalability in cloud environments.

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