End To End Machine Learning With Google Cloud

1. End To End
Machine
Learning With
Google Cloud
Tu Pham
CTO @ Eway JSC
Data Science and
Machine Learning Connect
-- Google Thailand --
Google IO Extended 2018
-- Ha Noi, Viet Nam --

2. About Me Tu Pham
- Google Developer Expert on Cloud
Platform
- CTO at Eway JSC
- Open source contributor, blogger,
father
- 8 years experience on Big data
and Cloud Computing

3. Since 2016
- Google IO Extended
- Google Next Extended
- Devfest Viet Nam
- Study Jam
- And so many events …
Our biggest event in Viet Nam
got > 1600 attendees
About Google
Cloud Platform
Viet Nam
Community

4. About Eway
- AdFlex.asia: Top 1 CPA network in Viet Nam
- MasOffer.net: Top 1 CPS network in Viet Nam
- DYNO.vn: Big data services for Fintech and
Online Advertising
- iHR: Big data for recruiting
- eDoctor.io: Platform for healthcare

5. One challenge

11. In 2013:
We have small data, manual
workflow
And … a lot of people for
manual everything
Migrate to
Google Cloud
from 2013

13. Our Common
Task in 2013

14. Our Very
First Data
Flow in
2013
1. Remember where I save the data
2. Use tools to extract a part data (SQL, Spark, Pandas,
...)
3. Download the data then send it to the data science
team
4. Wait for the model then manual apply by deploy as
services
5. If something wrong, back to step 1

15. What I Learned

16. Redesign The
Flow
Principles:
- KISS (Keep it simple, stupid)
- DRY (Don’t Repeat Yourself)
- Single Responsibility
- Low Cost
- Scalable

17. Become Geek

18. Redesign The
Flow
1. GC Compute Engine instances collect
raw data
2. GC Compute Engine instances convert
raw data to Apache Parquet files
3. GC Compute Engine upload parquet file
to GC Cloud Storage
4. Explore dataset using GC Datalab
5. Develop a machine learning model in
Tensorflow / Scikit learn
6. Train a machine learning model at
scale on GC Cloud ML Engine
7. Deploy the trained ML model by Web
API to GC Compute Engine instances
8. Expose Web API via GC Load Balancing

19. End-to-end ML with Google Cloud

20. Step 1: GC Compute Engine Instances
Collect Raw Data
- Technology: Cloud Load Balancing, Compute Engine
- Why Cloud Load Balancing:
- TCP/UDP Load Balancing
- Seamless Autoscaling
- Scalable
- Why Compute Engine:
- High-Performance
- Scalable
- Low Cost
- Fast Networking
- Custom Machine Types

21. Step 2: GC Compute Engine Instances
Convert Raw Data To Apache Parquet Files
- Technology: Compute Engine, Parquet file format
- Why Parquet:
- Self-describing
- Columnar storage format
- Language-independent
- High query-performance
- Spark SQL is much faster with Parquet
- High compression (up to 70%)- less disk IO

22. - Technology: Compute Engine, Parquet file format, Cloud Storage
- Why Cloud Storage:
- Four storage classes
- Easy to integrate
- Object Lifecycle Management
- Fast Networking
Step 3: GC Compute Engine Upload Parquet
File To GC Cloud Storage

23. Step 4: Explore Dataset Using GC Datalab
- Technology: Cloud Datalab
- Why Datalab:
- Integrated with: Cloud BigQuery, Cloud Machine Learning Engine, Cloud Storage, and
Stackdriver Monitoring
- IPython Support & Notebook Format
- Interactive Data Visualization
- Multi-Language Support: Python, SQL, and JavaScript (for BigQuery user-defined functions

24. - Technology: Tensorflow / Scikit Learn
- Why Tensorflow:
- Deep learning neural networks
- Huge community: contributors, researchers, developers
- Train models fast
- Allows developers to iterate quickly
- Run on large scale server
- Multi language support
Step 5: Develop Machine Learning Models
In Tensorflow / Scikit Learn

25. Step 6: Train Machine Learning Models At
Scale On GC Machine Learning Engine
- Technology: Cloud Machine Learning Engine
- Why Machine Learning Engine:
- Automatic Resource Provisioning
- Server-Side Preprocessing
- HyperTune
- Portable Models
- Integrated: Cloud Dataflow, Cloud Storage
- Multiple Frameworks: scikit-learn, XGBoost, Keras, TensorFlow

26. Be 1% better everyday tips
- Plan your system principles
- Single responsibility for everything
- Design system architecture, data flow, data model, data
structure first
- Separate realtime and batch flows
- Separate data storage strategies between data types
- Save the cost by network cost, instances cost, storage
cost by metric monitoring & alert system

27. Join The Flight
● Eway:
○ Senior / Experience Java Backend
○ Senior / Experience PHP FullStack
○ Senior / Experience Data Scientist
○ System Admin
○ Product Operation Executive
○ Business Analysis

28. Join The Flight
● Eway: hr@eway.vn
● Me: tupp@eway.vn