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Serverless Machine Learning on AWS - AWS Rome Meetup
- 1. Serverless Machine Learning on Amazon Web Services clda.co/aws-‐roma 10/15/2016 AWS @ Rome Applicazioni di Intelligenza Ar:ficiale con AWS Lambda
- 2. @alex_casalboni clda.co/aws-‐roma AWS @ Rome Web Developer (6+ years) Sr. SoFware Engineer @ Cloud Academy Master in Computer Science About me
- 3. What is Machine Learning? Back to 1959 (Arthur Samuel) How computers learn from Data How to solve decision problems AWS @ Romeclda.co/aws-‐roma
- 4. Machine Learning pipeline Training Predic3on batch real-‐Cme Feature extrac3on batch data informa:on features ML models AWS @ Romeclda.co/aws-‐roma
- 5. ? Machine Learning taxonomy Supervised Learning Unsupervised Learning AWS @ Romeclda.co/aws-‐roma
- 6. ? Machine Learning taxonomy classifica9on regression 170 cm Supervised Learning Unsupervised Learning AWS @ Romeclda.co/aws-‐roma
- 7. Machine Learning taxonomy Supervised Learning Unsupervised Learning AWS @ Romeclda.co/aws-‐roma
- 8. Machine Learning taxonomy clustering rule extrac9on group A group B A, B C Supervised Learning Unsupervised Learning AWS @ Romeclda.co/aws-‐roma
- 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 AWS @ Romeclda.co/aws-‐roma
- 10. What problems can ML solve for you? Supervised Learning Unsupervised Learning classifica'on regression clustering rule extrac'on ? fraud detec:on 170 cm gro gro A, B C price of a stock over :me purchase likelihood user segmenta:on AWS @ Romeclda.co/aws-‐roma
- 11. Learning Data Machine Cloud Big Science Information Internet Statistics Technology Python Future Mining Social Deep IOT Algorithms Management Storage Petabytes Parallel Network Privacy Million NoSQL PaaS SQL Database Exabytes Billion Dataset Hadoop R AWS @ Romeclda.co/aws-‐roma
- 12. 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 AWS @ Romeclda.co/aws-‐roma
- 13. … and it keeps geMng bigger! AWS @ Romeclda.co/aws-‐roma
- 14. Big data challenges Manual explora:on is not an op:on Data-‐driven decisions are a must Distributed/parallel compu:ng The curse of dimensionality AWS @ Romeclda.co/aws-‐roma
- 17. Why is deploying ML models a challenge? AWS @ Romeclda.co/aws-‐roma
- 18. Why is deploying ML models a challenge? AWS @ Romeclda.co/aws-‐roma + + Data Scien:st Data Time
- 19. Why is deploying ML models a challenge? AWS @ Romeclda.co/aws-‐roma + + Data Scien:st Data Time ML Model Data Visualisa:on Prototype + +
- 20. Why is deploying ML models a challenge? AWS @ Romeclda.co/aws-‐roma Produc:on Code + + Data Scien:st Data Time ML Model Data Visualisa:on Prototype + +
- 21. Why is deploying ML models a challenge? AWS @ Romeclda.co/aws-‐roma + + Data Scien:st Data Time ML Model Data Visualisa:on Prototype + + Web Developer DevOps A lot of Time + +
- 22. Why is deploying ML models a challenge? 1. Prototyping != Produc:on-‐ready 2. We need Elas:city 4. Mul:-‐model architectures 3. Too many nice-‐to-‐have features 5. Avoid lack of ownership AWS @ Romeclda.co/aws-‐roma
- 23. Machine Learning as a Service (MLaaS) Amazon Machine Learning Azure Machine Learning Google PredicCon API IMB Watson AnalyCcs BigML AWS @ Romeclda.co/aws-‐roma cloudacademy.com/blog/machine-‐learning
- 24. Amazon Machine Learning AmazonML One of the first MLaaS solu:ons (Apr 2015) It’s great for classifica:on and regression problems Only linear models (linear & logis:c regression + SGD) No support for advanced scenarios yet AWS @ Romeclda.co/aws-‐roma
- 25. AmazonML @ Cloud Academy clda.co/7-‐day-‐free (no credit card required!) AWS @ Romeclda.co/aws-‐roma
- 26. Serverless compuCng to the rescue! Transparent scalability, elas:city and availability Developer-‐friendly maintenance (versioning + aliases) AWS Lambda Event-‐driven approach & never pay for idle Microservices culture AWS @ Romeclda.co/aws-‐roma
- 27. Quick Example clda.co/ML-‐Lambda AWS @ Romeclda.co/aws-‐roma
- 28. AWS @ Rome Serverless ML @ Cloud Academy Mul:-‐model architecture RESTful interface for each ML model 1 Lambda Func:on for each ML model S3 + RDS for storage Periodic training (offline) Real-‐world Example clda.co/aws-‐roma
- 31. AWS @ Romeclda.co/aws-‐roma AWS Lambda 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) Cold start :me is long and hard to avoid Unit/integra:on tests help, but not enough LimitaCons of Serverless ML
- 32. Grazie =) cloudacademy.com Q & A 10/15/2016 AWS @ Rome