Building Serverless Machine Learning models in the Cloud

1. Building Serverless Machine Learning models in the Cloud clda.co/serverless-‐tokyo 10/01/2016 |⽇日本

2. ⾃自⼰己紹介 @alex_casalboni clda.co/serverless-‐tokyo |⽇日本 Web Developer (6+ years) Sr. SoDware Engineer @ Cloud Academy Master in Computer Science About Me

3. 真のデータサイエンティストとはどんな⼈人か？ |⽇日本 What does a real Data Scien5st look like? Data ScienIst Very smart & curious Numbers lover (i.e. Data) Great teamwork skills 40% analysis, 30% design, 30% code clda.co/serverless-‐tokyo

4. 機械学習のパイプラインで得られるものは何か？ |⽇日本 What’s the outcome of a ML pipeline? + + Data ScienIst Data Time clda.co/serverless-‐tokyo

5. 機械学習のパイプラインで得られるものは何か？ |⽇日本 What’s the outcome of a ML pipeline? + + Data ScienIst Data Time ML Model Data VisualisaIon Prototype + + clda.co/serverless-‐tokyo

6. 機械学習のパイプラインで得られるものは何か？ |⽇日本 What’s the outcome of a ML pipeline? ProducIon Code + + Data ScienIst Data Time ML Model Data VisualisaIon Prototype + + clda.co/serverless-‐tokyo

7. 機械学習のパイプラインで得られるものは何か？ |⽇日本 What’s the outcome of a ML pipeline? + + Data ScienIst Data Time ML Model Data VisualisaIon Prototype + + Web Developer DevOps A lot of Time + + clda.co/serverless-‐tokyo

8. オーナーシップの⽋欠如 |⽇日本 The Lack of Ownership != Data ScienIst DevOps Mathema7cal modeling Sta7s7cal analysis Data mining (Cloud) Opera7ons   System administra7on SoEware best prac7ces clda.co/serverless-‐tokyo

9. MLaaS はどうか？ |⽇日本 What about MLaaS? Machine Learning as a Service Data ExploraIon does not come for free Parameters tuning becomes blind guessing Very liZle control over your models You wouldn’t need a Data ScienIst at all, but… Not everybody likes black-‐box abstracIons clda.co/serverless-‐tokyo

10. 30⾏行のコードでどうやって機械学習のモデルが構築できるか |⽇日本 How do you build a ML model in 30 LOC? clda.co/ML-‐code clda.co/serverless-‐tokyo

11. |⽇日本clda.co/serverless-‐tokyo

12. 伝統的なデプロイ戦略 |⽇日本 Tradi5onal deployment strategies 1. Web-‐app controller how do you update your model(s)? same auth layer? shared uWSGI processes? Simplest soluIon, but… clda.co/serverless-‐tokyo

13. 伝統的なデプロイ戦略 |⽇日本 Tradi5onal deployment strategies 2. Fleet of servers same problems as before many more servers to maintain no elasIcity (over-‐provisioning) Bigger capacity and no code changes, but… Load Balancing clda.co/serverless-‐tokyo

14. 伝統的なデプロイ戦略 |⽇日本 Tradi5onal deployment strategies 3. Auto Scaling sIll shared resources? even bigger lack of ownership what about caching, versioning and auth? ElasIc and highly available, but… AWS ELB + Auto Scaling (or maybe ElasIc Beanstalk?) clda.co/serverless-‐tokyo

15. サーバレス機械学習 |⽇日本 Serverless Machine Learning Versioning, staging & caching 1 model = 1 microservice IntuiIve RESTful interface High Availability (no downIme) Very liZle operaIonal eﬀort Transparent elasIcity (PAYG) Failure isolaIon / DecentralisaIon Oﬄine training phase ProducIon-‐ready prototypes A/B tesIng through composiIon clda.co/serverless-‐tokyo

16. かんたんな例：サーバーレスに評判を分析する |⽇日本 A simple example: Serverless Sen5ment Analysis clda.co/ML-‐Lambda clda.co/serverless-‐tokyo

17. もっと実践的な例：Cloud Academyにおけるサーバーレスな機械学習事例 |⽇日本 A real example: Serverless ML @ Cloud Academy Serverless ML @ Cloud Academy MulI-‐model architecture RESTful interface for each ML model 1 Lambda FuncIon for each ML model S3 + RDS for storage Periodic training (oﬄine) clda.co/serverless-‐tokyo

20. サーバーレスな機械学習における制約事項 |⽇日本 Limita5ons of Serverless ML clda.co/serverless-‐tokyo AWS Lambda No real-‐Ime models (only pseudo real-‐Ime) Deployment package management: size limit and OS libraries Not suitable for model training yet (5 min max execuIon Ime) Cold start Ime is long and hard to avoid Unit/integraIon tests help, but not enough

21. ご静聴ありがとうございました 10/01/2016 Thank you! |⽇日本 clda.co/TokyoServerlessConf 30% OFF

Building Serverless Machine Learning models in the Cloud

Alex Casalboni

Building Serverless Machine Learning models in the Cloud