From Data Science to Production - deploy, scale, enjoy! / PyData Amsterdam - Mar 12, 2016

From Data Science to Production

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deploy, scale, enjoy!

Sergii Khomenko, Data Scientistsergii.khomenko@stylight.com, @lc0d3r

PyData Amsterdam - March 12, 2016

Sergii Khomenko

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Data scientist at one of the biggest fashion communities, Stylight.

Data analysis and visualisation hobbyist, working on problems not only in working time but in free time for fun and personal data visualisations.

Originally from computer engineering background.

Speaker at Berlin Buzzwords 2014, ApacheCon Europe 2014, Puppet Camp London 2015, Berlin Buzzwords 2015 , Tableau Conference on Tour 2015, Budapest BI Forum 2015, Crunchsconf 2015, FOSDEM 2016

Fellow DevOps

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Quentin NerdenMilos Radovanovic Patrick Roelke

Profitable LeadsStylight provides its partners with high-quality leads enabling partner shops to leverage Stylight as a ROI positive traffic channel.

InspirationStylight offers

shoppable inspiration that

makes it easy to know what to

buy and how to style it.

Branding & ReachStylight offers a unique opportunity for brands to reach an audience that is actively looking for style online.

ShoppingStylight helps users search

and shop fashion and lifestyle products smarter across

hundreds of shops.

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Stylight – Make Style HappenCore Target Group

Stylight help aspiring women between 18 and 35 to evolve their style through shoppable inspiration.

Stylight – acting on a global scale

Experienced & Ambitious Team

Innovative cross-functional organisation with flat hierarchy builds a unique team spirit.

• +200 employees• 40 PhDs/Engineers• 28 years average age

• 63% female• 23 nationalities• 0 suits

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D a t a S c i e n t i s t : P e r s o n w h o i s b e t t e r a t s t a t i s t i c s t h a n a n y

s o f t w a r e e n g i n e e r a n d b e t t e r a t s o f t w a r e e n g i n e e r i n g t h a n a n y

s t a t i s t i c i a n .

Agenda

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E a r l y d a y s o f s t a r t u p s S o f t w a r e e n g i n e e r i n g

I m m u t a b l e i n f r a s t r u c t u r e S e r v e r l e s s a r c h i t e c t u r e

The Early Days of Startups

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Problem definition:

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• Many different technologies • Hard to reproduce data science results • Issues with backward compatibility • Dependency hell • Hard to scale products • Hard to on-board new people

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Software engineering

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built circa 2015-16

Our stack

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You most likely doing it already

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• Version control • Cover code with tests • nosetests, pytest, unittest2 - start small with doc tests - try out TDD: rednose, nose-watch

You most likely doing it already

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• Cover code with tests • yes, even your R application could

have tests - testthat - devtools

• Code reviews • Pair programming

Some of the mentioned problems

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• Many different technologies • Issues with backward compatibility • Dependency hell • Hard to on-board new people

18image from http://udaypal.com/

19image from http://udaypal.com/

20image from http://udaypal.com/

Some of the mentioned problems

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• Many different technologies • Issues with backward compatibility • Dependency hell • Hard to on-board new people

How it could help:

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• Every technology has its own container - just docker run

• Every package with version defined in Dockerfile - have a base image for more advanced cases

• New people - just docker run

23image from http://udaypal.com/

r-base/Dockerfile

24image from http://udaypal.com/

lc0/docker-shiny-server

25image from http://udaypal.com/

Known issues

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• Images could be really huge • Try to skip anything you do not need • Alpine Linux as a base image • 5 mb base image (musl libc and BusyBox)

• Iron.io has pre-built images based on alpine • python, scala, java, elixir, etc

Known issues

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16 mb

232 mb

Some of the mentioned problems

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• Hard to roll out • Hard to maintain production dependencies

29image from http://udaypal.com/AWS ECR

30image from http://udaypal.com/

31image from http://udaypal.com/

CircleCI deployments

32image from http://udaypal.com/

CircleCI deployments

33image from http://udaypal.com/

CircleCI deployments

34image from http://udaypal.com/

CircleCI deployments

Immutable infrastructure

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Infrastructure as Code

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N e e d t o u p g r a d e ? N o p r o b l e m . B u i l d a n e w , u p g r a d e d s y s t e m a n d t h r o w t h e o l d o n e a w a y . N e w a p p

r e v i s i o n ? S a m e t h i n g . B u i l d a s e r v e r ( o r i m a g e ) w i t h a n e w

r e v i s i o n a n d t h r o w a w a y t h e o l d o n e s .

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CloudFormation

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CloudFormation

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cloudtools/troposphere

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cloudtools/troposphere

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cloudtools/troposphere

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Terraform

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Terraform

Kubernetes and Docker {Swarm, Compose}

Serverless architecture

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Possibilities

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• all Lambdas in one place with version control • integration tests with real events • proper CI/CD setup

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CircleCI deployments

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CircleCI deployments

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CircleCI deployments

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Cloud functions

Use-case of outlier detection

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custom unificationpipeline

DepartmentsBusiness

Intelligence

internal processes variety of event types and structures

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Outlier detection to Slack

www.stylight.com

sergii.khomenko@stylight.com@lc0d3r

Related links

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1. Testing Your Code - The Hitchhiker's Guide to Python

2. https://hub.docker.com/_/r-base/

3. http://www.alpinelinux.org/

4. https://github.com/iron-io/dockers

5. Docker Hub: A new stack plus ecosystem partners automate developer workflows

6. Trash Your Servers and Burn Your Code: Immutable Infrastructure and Disposable Components

Related links

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7. https://github.com/cloudtools/troposphere

8. CloudFormation UpdatePolicy Attribute

9. https://www.terraform.io/

10.(Docker Compose + Docker Swarm) or Kubernetes

11.Google Cloud Functions

12.https://github.com/apex/apex

13.Streaming Data Processing with Amazon Kinesis and AWS Lambda

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