Speak with Alex Casalboni, Roberto Turrin and Luca Baroffio in our Engineering team at Cloud Academy, and learn how they use AWS to manage daily challenges and build a machine learning system.
Exploring the Future Potential of AI-Enabled Smartphone Processors
Cloud Academy & AWS: how we use Amazon Web Services for machine learning and data collection
1. Cloud Academy & AWS:
how we use Amazon Web Services
for machine learning and data collec:on
cloudacademy.com
4/27/2016
2. About us
Alex Casalboni Roberto Turrin Luca Baroffio
Sr. SoCware Engineer Sr. Data Scien:st (PhD) Data Scien:st (PhD)
@alex_casalboni @robytur @lucabaroffio
clda.co/webinar-ML
3. What is Machine Learning (ML)?
Back to 1959 (A. Samuel)
Decision problems that
can be modeled from data
clda.co/webinar-ML
4. Machine Learning pipeline
Training Predic1on
batch real-‐:me
Feature
extrac1on
batch
data informaGon
features ML models
clda.co/webinar-ML
9. What problems can ML solve for you?
Supervised
Learning
Unsupervised
Learning
classifica'on
regression
clustering
rule extrac'on
?
170
cm
gro gro
A, B C
clda.co/webinar-ML
10. What problems can ML solve for you?
Supervised
Learning
Unsupervised
Learning
classifica'on
regression
clustering
rule extrac'on
?
fraud detecGon
170
cm
gro gro
A, B C
price of a stock over Gme
purchase likelihood
user segmentaGon
clda.co/webinar-ML
12. Machine learning and Big data
“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
clda.co/webinar-ML
15. Why is deploying ML models a challenge?
1. Prototyping != Produc=on-‐ready
2. We need Elas=city
4. Avoid lack of ownership
clda.co/webinar-ML
3. Too many nice-‐to-‐have features
16. Where is the lack of ownership?
clda.co/webinar-ML
!=
Data Scien=st DevOps
Machine Learning
Data mining
Sta:s:cal analysis
System administra:on
(Cloud) Opera:ons
SoCware engineering
17. Many op:ons and tools offered by AWS
ELB Auto Scaling
Elas:c
Beanstalk
Amazon
ML
ECS
EMR LambdaEC2
API
Gateway
clda.co/webinar-ML
18. Serverless compu:ng to the rescue!
Transparent scalability, elas=city and availability
Developer-‐friendly maintenance (versioning + aliases)
AWS
Lambda
Event-‐driven approach & never pay for idle
1 func=on = 1 model
clda.co/webinar-ML
A/B tes=ng via composi=on
19. How is “Serverless” possible?
There is always a server somewhere,
you just don't have to worry about it :)
clda.co/webinar-ML
20. AWS Lambda + Amazon API Gateway
+
AWS
Lambda
API
Gateway
RESTful & auth layer
Global CDN and caching (CloudFront)
Staging & versioning & mocking
API Decoupling
clda.co/webinar-ML
23. AWS Lambda limita:ons
clda.co/webinar-ML
No real-‐=me models (only pseudo real-‐=me)
Deployment package management: size limit and OS libraries
Not suitable for model training yet (5 min max execu=on =me)AWS
Lambda
24. What about Amazon Machine Learning?
clda.co/webinar-ML
Amazon
ML
One of the first MLaaS solu=ons (1 year old)
Great service for classifica=on and regression
Only linear models (linear & logis=c regression + SGD)
No support for advanced scenarios yet
(collabora=ve recommenda=on, mul=media, online learning, etc.)
25. Key Takeaways
clda.co/webinar-ML
Data-‐driven decision and user-‐centered ML will make your product smarter
Maximize ownership by removing obstacles btw prototype and produc=on
Eliminate tradeoffs btw high-‐scalability and nice-‐to-‐have features
Go Serverless and stop worrying about Ops
MLaaS makes your life even simpler, unless you need more control
