1. Deploy ML in Data Product
December 2015

2. Motivation
far more time spent on deploying a model compared to developing the model
My engineering and industry background motivates me to use ML in real products

3. Data Science Domain
Data
Engineering
Machine Learning

4. Standard ML Model tweak it 70% Training Set Model Labelled Data OK
Test Set
30%

5. Data Set Boston House Prices
Concerns house prices in suburbs of Boston.
Data Set Characteristics:   Multivariate
Attribute Characteristics: Categorical, Integer, Real
Associated Tasks: Regression
Number of Instances: 506
Number of Attributes: 14
Missing Values? No
Date Donated 07/07/93
Number of Web Hits:
courtesy of StatLib library at Carnegie Mellon University

6. Demo with notebook

7. Our flow 100% Labelled Data Training Set Model Save Development Domain
Load
Predict
Deployment Domain

8. What it is not frequency of predicts is small no adaptive learning
<1000 predictions per hour
single prediction machine is OK
model does not need to be optimised to run in parallel
no adaptive learning
in real life and absolute MUST HAVE
example shows how outdated $$ amounts are

9. System Architecture Single Page App under AngularJS ISP
predict request
with data Object
ISP
for app hosting
HTML, CSS, JS
classifier as
return Object
Flask
microframework
Python, scikit-learn

10. Client side

11. the ML loop

12. Console.log

13. ML Server side no web app server side details shown

16. SSH connection with AWS
apt-get update
apt-get install -y python-software-properties python g++ make
add-apt-repository ppa:chris-lea/node.js
apt-get install nodejs
apt-get install git
npm install forever –g
apt-get install apache2
apt-get install libapache2-mod-wsgi
apt-get install python-pip
pip install flask
Web Server Gateway Interface

19. DEMO

20. Lessons learnt simple product demonstrated long way to go
ML model could be much more sophisticated
long way to go
automate deployment
security
scaling would need a new architecture server side
what about the vendors who propose solutions
AWS, Azure, Dato, etc. ....

21. Q&A