google apac machine learning expert day
Post on 22-Jan-2018
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Google APAC Machine Learning Expert Day
Linkernetworks - Evan Lin / Benjamin Chen
● Tensorflow Summit Recap ● Google APAC Machine Learning Expert Day● Our lightening talk (Linker Neworks)
Agenda
Who is Evan Lin
● Daily Work:
○ Linker Networks : Cloud
Architect in
● Community:
○ Co-Organizer in Golang
Taipei User Group
● Habit:
○ Project 52
Tensorflow Summit RECAP
Tensorflow Dev Summit 2017
link
Benjamin ChenLinker Networks
Data ScientistTaiwan R User Group
Officer
benjamin0901@gmail.com
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
TensorFlow Wide & Deep Learning
Wide Model Deep Model
Memorization Generalization
Revelance Diversity
Deep ModelGeneralization
Diversity
Wide ModelMemorization
Relevance
Wide & Deep Model
Classify cucumbers with tensorflow
Japanese Idol with DCGAN (link)
APAC Machine Learning Expert Day 1
Some Interesting Projects
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
APAC Machine Learning Expert Day 2
Google Cloud Codelab
Classify Manhattan
Classify MNIST images
Linker NetworksWhen Kubernetes meets Tensorflow
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
Utilization Prediction- Product: Cluster of Machine
Intelligence, CMI- Goals:
- Predict CPU and memory trend
- Auto-scaling
- Algorithm: LSTM- Module: Keras- Trying to tune threshold
Back to Evan
Eliminate engineering bottlenecks
in Machine Learning
Data Collect Probe & Sensor & Smart GW
Vizualization
Data Process
Data Analysis &Machine Learning DC/OS Spark ML Tensorflow
DC/OS
StorageCassandra
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
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
AMIP Architecture
Linker is hiringCloud Platform Developer
- Familiar with DCOS/K8S- Strong DevOps experience
Q&A
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