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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
- 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