How to deploy machine learning models in the Cloud

•

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Developing and experimenting with machine learning models in Python is easy and well supported by robust and agile libraries such as scikit-learn, although efficiently deploying multi-model systems at scale is still a challenge in the data science field. This talk will focus on the main issues related to deploying machine learning models and how to make scikit-learn production-ready with minimal operational efforts, by means of Cloud Computing services, in particular Amazon Web Services. Prerequisites: basic Machine Learning understanding (modeling and training), minimal knowledge about scikit-learn and Python utilities such as Pandas and boto.

How to deploy Machine Learning
models in the Cloud
Alex Casalboni
PYCON SETTE
@ Firenze
clda.co/pycon7-alex

About Me
PYCON SETTE @ FIRENZE
@alex_casalboni
clda.co/pycon7-alex
Bachelor in Computer Science
Master in Sound & Music Engineering
6+ years of Web Development
3+ years of Python

Have you ever met a Data Scien@st?
PYCON SETTE @ FIRENZE
Bob
Bob leads a skilled team of data scienCsts
Bob has built a great ML model
Bob loves Python
Bob likes user-‐centered ML
clda.co/pycon7-alex

How did Bob build his model in Python?
PYCON SETTE @ FIRENZE
clda.co/pycon7-code
clda.co/pycon7-alex
Bob

PYCON SETTE @ FIRENZE
GPU availability
distributed compuCng
infrastructure-‐aware code
1. Bob needs Elas@city
clda.co/pycon7-alex
And why is deploying a challenge?
Bob

PYCON SETTE @ FIRENZE
MulC-‐model architecture
A/B tesCng & versioning
RESTful API
2. Bob needs plenty of Features
Auth layer
clda.co/pycon7-alex
And why is deploying a challenge?
Bob

PYCON SETTE @ FIRENZE
infrastructure maintenance
conCnuous delivery & deployments
automaCon, performance & monitoring
3. Data Scien@sts ain’t DevOps
≠
clda.co/pycon7-alex
And why is deploying a challenge?
Data
ScienCst
DevOps

PYCON SETTE @ FIRENZE
model design, training & tesCng
code reviews & debugging
data exploraCon
4. DevOps ain’t Data Scien@sts
≠
clda.co/pycon7-alex
And why is deploying a challenge?
Data
ScienCst
DevOps

Deployment Strategies
PYCON SETTE @ FIRENZE
1. Web-‐app controller
clda.co/pycon7-alex
how will Bob update his model(s)?
same website auth layer?
shared (limited) uWSGI processes?
It’s disarmingly simple, but…
Bob

Deployment Strategies
PYCON SETTE @ FIRENZE
2. Fleet of servers
clda.co/pycon7-alex
same problems as before
Bob has many more machines to maintain
sCll no elasCcity (over-‐provisioning)
Bigger capacity and no code changes, but…
Bob Bob2 Bob3
Load Balancing

Deployment Strategies
PYCON SETTE @ FIRENZE
3. Auto Scaling
clda.co/pycon7-alex
<Bob>
sCll shared resources? containers?
even bigger lack of ownership
what about caching, versioning and auth?
Bob achieved elasCcity and availability , but…
AWS ELB + Auto Scaling
(or maybe ElasCc Beanstalk?)

Deployment Strategies
PYCON SETTE @ FIRENZE
4. Serverless
clda.co/pycon7-alex
+
Amazon API
Gateway
AWS
Lambda
Happy
Bob
Inﬁnite scalability, global CDN and caching
Transparent elasCcity (and PAYG)
RESTful & auth layer + versioning (for free!)
Bob doesn’t need to be a DevOps anymore!

The Serverless Cloud
PYCON SETTE @ FIRENZEclda.co/pycon7-alex
There is always a server somewhere, 
you just don't have to worry about it :)

Does Bob’s code change a lot?
PYCON SETTE @ FIRENZEclda.co/pycon7-alex
Basically, no change!
Bob

How does Bob keep the model updated?
PYCON SETTE @ FIRENZEclda.co/pycon7-alex
Bob
Hint: use gzip!

PYCON SETTE @ FIRENZE
clda.co/pycon7-lambda
clda.co/pycon7-alex

Serverless Cloud Domina@on
PYCON SETTE @ FIRENZE
Amazon Web Services (Lambda)
Microso` Azure (FuncCons)
Google Cloud Plaborm (FuncCons)
IBM Bluemix (OpenWhisk)
hook.io
Cloud PlaSorms Hybrid & Microservices
stackhut.com
webtask.io
iron.io
clda.co/pycon7-alex

Thank you :)
jobs.cloudacademy.com
P.S. we are hiring!

What is Machine Learning and how does it work? But even more importantly, what problems can ML solve for you and your company? Once you have understood the potential use cases, we will briefly describe the main challenges in the world of Big Data. Why is deploying ML models so hard and how can Cloud Computing help? Many MLaaS options are available on the market (AWS, Google, Azure, BigML, etc.). We will see how they compare to each other and which may best fit your needs. Whenever MLaaS is not enough, you can build your own ML models. We will briefly explain why Serverless is a great deployment strategy for this use case and what problems and limitation arise with it. Furthermore, we will put these ideas into practice and build a model for Sentiment Analysis, based on Python (scikit-learn), and trained with a public dataset by Stanford University.

Building Serverless Machine Learning Models in the Cloud [PyData DC]

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How to deploy machine learning models in the Cloud

1. How to deploy Machine Learning models in the Cloud Alex Casalboni PYCON SETTE @ Firenze clda.co/pycon7-alex

2. About Me PYCON SETTE @ FIRENZE @alex_casalboni clda.co/pycon7-alex Bachelor in Computer Science Master in Sound & Music Engineering 6+ years of Web Development 3+ years of Python

3. Have you ever met a Data Scien@st? PYCON SETTE @ FIRENZE Bob Bob leads a skilled team of data scienCsts Bob has built a great ML model Bob loves Python Bob likes user-‐centered ML clda.co/pycon7-alex

4. How did Bob build his model in Python? PYCON SETTE @ FIRENZE clda.co/pycon7-code clda.co/pycon7-alex Bob

5. PYCON SETTE @ FIRENZE GPU availability distributed compuCng infrastructure-‐aware code 1. Bob needs Elas@city clda.co/pycon7-alex And why is deploying a challenge? Bob

6. PYCON SETTE @ FIRENZE MulC-‐model architecture A/B tesCng & versioning RESTful API 2. Bob needs plenty of Features Auth layer clda.co/pycon7-alex And why is deploying a challenge? Bob

7. PYCON SETTE @ FIRENZE infrastructure maintenance conCnuous delivery & deployments automaCon, performance & monitoring 3. Data Scien@sts ain’t DevOps ≠ clda.co/pycon7-alex And why is deploying a challenge? Data ScienCst DevOps

8. PYCON SETTE @ FIRENZE model design, training & tesCng code reviews & debugging data exploraCon 4. DevOps ain’t Data Scien@sts ≠ clda.co/pycon7-alex And why is deploying a challenge? Data ScienCst DevOps

9. Deployment Strategies PYCON SETTE @ FIRENZE 1. Web-‐app controller clda.co/pycon7-alex how will Bob update his model(s)? same website auth layer? shared (limited) uWSGI processes? It’s disarmingly simple, but… Bob

10. Deployment Strategies PYCON SETTE @ FIRENZE 2. Fleet of servers clda.co/pycon7-alex same problems as before Bob has many more machines to maintain sCll no elasCcity (over-‐provisioning) Bigger capacity and no code changes, but… Bob Bob2 Bob3 Load Balancing

11. Deployment Strategies PYCON SETTE @ FIRENZE 3. Auto Scaling clda.co/pycon7-alex <Bob> sCll shared resources? containers? even bigger lack of ownership what about caching, versioning and auth? Bob achieved elasCcity and availability , but… AWS ELB + Auto Scaling (or maybe ElasCc Beanstalk?)

12. Deployment Strategies PYCON SETTE @ FIRENZE 4. Serverless clda.co/pycon7-alex + Amazon API Gateway AWS Lambda Happy Bob Inﬁnite scalability, global CDN and caching Transparent elasCcity (and PAYG) RESTful & auth layer + versioning (for free!) Bob doesn’t need to be a DevOps anymore!

13. The Serverless Cloud PYCON SETTE @ FIRENZEclda.co/pycon7-alex There is always a server somewhere,  you just don't have to worry about it :)

14. Does Bob’s code change a lot? PYCON SETTE @ FIRENZEclda.co/pycon7-alex Basically, no change! Bob

15. How does Bob keep the model updated? PYCON SETTE @ FIRENZEclda.co/pycon7-alex Bob Hint: use gzip!

16. PYCON SETTE @ FIRENZE clda.co/pycon7-lambda clda.co/pycon7-alex

17. Serverless Cloud Domina@on PYCON SETTE @ FIRENZE Amazon Web Services (Lambda) Microso` Azure (FuncCons) Google Cloud Plaborm (FuncCons) IBM Bluemix (OpenWhisk) hook.io Cloud PlaSorms Hybrid & Microservices stackhut.com webtask.io iron.io clda.co/pycon7-alex

18. Thank you :) jobs.cloudacademy.com P.S. we are hiring!

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