Serverless Machine Learning on AWS - Serverless Meetup Milano

Serverless Machine Learning on Amazon Web Services

clda.co/serverless-‐milano11/03/2016

Applicazioni di Intelligenza Ar:ficiale con AWS Lambda

http://clda.co/serverless-milano

@alex_casalboni

clda.co/serverless-‐milano Serverless Meetup @ Milan

Web Developer (6+ years)

Sr. So;ware Engineer @ Cloud Academy

Master in Computer Science

About me

https://twitter.com/alex_casalboni

https://twitter.com/alex_casalboni


What is Machine Learning?

Back to 1959 (Arthur Samuel)

How computers learn from Data

How to solve decision problems

Serverless Meetup @ Milanclda.co/serverless-‐milano


Machine Learning pipeline

Training Predic3on

batch real-‐Ame

Feature extrac3on

batch

data informaMon

features ML models



?Machine Learning taxonomy

classifica2on

regression 170cm

Supervised Learning

Unsupervised Learning



Machine Learning taxonomy

clustering

rule extrac2on

group A group B

A, B C

Supervised Learning




What problems can ML solve for you?

Supervised Learning


classifica'on

regression

clustering

rule extrac'on

?

170cm

gro gro

A, B C



What problems can ML solve for you?

Supervised Learning


classifica'on

regression

clustering

rule extrac'on

?fraud detecMon

170cm

gro gro

A, B C

price of a stock over Mme

purchase likelihood

user segmentaMon



LearningDataMachine

Cloud

Big

Science

Information

Internet

Statistics

Technology

Python Future

Mining Social

Deep

IOT

AlgorithmsManagement

Storage Petabytes

Parallel

Network

Privacy

MillionNoSQL

PaaS

SQL

Database

Exabytes

Billion

Dataset

Hadoop

R



Generated data started growing ~10 years ago…

“90% of the data in the world today has been created in the last two years alone” -‐ IBM

“300+ hours worth of video content is being uploaded to the site every minute” -‐ Youtube



… and it keeps geKng bigger!



What does a real Data ScienAst look like?


Data ScienMst

Very smart & curious

Numbers lover (i.e. Data)

Great teamwork skills

40% analysis, 30% design, 30% code


Big data challenges

Manual exploraMon is not an opMon

Data-‐driven decisions are a must

Distributed/parallel compuMng

The curse of dimensionality


https://en.wikipedia.org/wiki/Curse_of_dimensionality


Why is deploying ML models a challenge?




+

+

Data ScienMst

Data

Time




+

+

Data ScienMst

Data

Time

ML Model

Data VisualisaMon

Prototype

+

+




ProducMon Code

+

+

Data ScienMst

Data

Time

ML Model

Data VisualisaMon

Prototype

+

+




+

+

Data ScienMst

Data

Time

ML Model

Data VisualisaMon

Prototype

+

+

Web Developer

DevOps

A lot of Time

+

+




1. Prototyping != ProducMon-‐ready

2. We need ElasMcity

4. MulM-‐model architectures

3. Too many nice-‐to-‐have features

5. Avoid lack of ownership



The Lack of Ownership


!=

Data ScienMst DevOps

MathemaAcal modeling StaAsAcal analysis

Data mining

(Cloud) OperaAons System administraAon SoVware best pracAces


Machine Learning as a Service (MLaaS)

AmazonMachine Learning

AzureMachine Learning

GooglePredicAon API

IMBWatson AnalyAcs

BigML

cloudacademy.com/blog/machine-‐learning


http://cloudacademy.com/blog/aws-machine-learning/









































http://cloudacademy.com/blog/azure-machine-learning/






































http://cloudacademy.com/blog/google-prediction-api/

http://cloudacademy.com/blog/bigml-machine-learning/
























































































































































https://www.ibm.com/analytics/watson-analytics/
















































































































































































































































http://cloudacademy.com/blog/machine-learning/


Amazon Machine Learning

AmazonML

One of the first MLaaS soluMons (Apr 2015)

It’s great for classificaMon and regression problems

Only linear models (linear & logisMc regression + SGD)

No support for advanced scenarios yet



Serverless compuAng to the rescue!


Versioning, staging & caching

1 model = 1 microservice

Flexible RESTful interface

High Availability (no downMme)

Very liele operaMonal effort

Transparent elasMcity (PAYG)

Failure isolaMon / DecentralisaMon Offline training phase

ProducMon-‐ready prototypes A/B tesMng through composiMon


Quick Example

clda.co/ML-‐Lambda


https://d2ya98r07n6vbt.cloudfront.net/sentiment.html

http://clda.co/ML-Lambda


Serverless ML @ Cloud Academy

MulM-‐model architecture

1 Lambda FuncMon for each ML model

S3 to store models (1~10MB each)

RDS to store training data (PostgreSQL)

Periodic training (offline)

Real-‐world Example



AWS Lambda

No real-‐Mme models (only pseudo real-‐Mme)

Deployment package management: size limit and OS libraries

Not suitable for model training yet (5 min max execuMon Mme)

Cold start Mme is long and hard to avoid

Unit/integraMon tests help, but not enough

LimitaAons of Serverless ML



cloudacademy.com/blog/machine-‐learning

cloudacademy.com/blog/serverless

cloudacademy.com/webinars

cloudacademy.com/community

AddiAonal Resources


http://cloudacademy.com/blog/machine-learning/

http://cloudacademy.com/blog/serverless

http://cloudacademy.com/webinars

http://cloudacademy.com/community


Grazie =)

cloudacademy.com

Q & A

11/03/2016

https://cloudacademy.com/

Serverless Machine Learning on AWS - Serverless Meetup Milano

Engineering

Transcript of Serverless Machine Learning on AWS - Serverless Meetup Milano